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Dissertations / Theses on the topic 'Machine learning'
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Andersson, Viktor. "Machine Learning in Logistics: Machine Learning Algorithms : Data Preprocessing and Machine Learning Algorithms." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64721.
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Data Ductus is a Swedish IT-consultant company, their customer base ranging from small startups to large scale cooperations. The company has steadily grown since the 80s and has established offices in both Sweden and the US. With the help of machine learning, this project will present a possible solution to the errors caused by the human factor in the logistic business.A way of preprocessing data before applying it to a machine learning algorithm, as well as a couple of algorithms to use will be presented.<br>Data Ductus är ett svenskt IT-konsultbolag, deras kundbas sträcker sig från små startups till stora redan etablerade företag. Företaget har stadigt växt sedan 80-talet och har etablerat kontor både i Sverige och i USA. Med hjälp av maskininlärning kommer detta projket att presentera en möjlig lösning på de fel som kan uppstå inom logistikverksamheten, orsakade av den mänskliga faktorn.Ett sätt att förbehandla data innan den tillämpas på en maskininlärning algoritm, liksom ett par algoritmer för användning kommer att presenteras.
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Dinakar, Karthik. "Lensing Machines : representing perspective in machine learning." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112523.
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Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.<br>Cataloged from PDF version of thesis. Due to the condition of the original material with text runs off the edges of the pages, the reproduction may have unavoidable flaws.<br>Includes bibliographical references (pages 167-172).<br>Generative models are venerated as full probabilistic models that randomly generate observable data given a set of latent variables that cannot be directly observed. They can be used to simulate values for variables in the model, allowing analysis by synthesis or model criticism, towards an iterative cycle of model specification, estimation, and critique. However, many datasets represent a combination of several viewpoints - different ways of looking at the same data that leads to various generalizations. For example, a corpus that has data generated by multiple people may be mixtures of several perspectives and can be viewed with different opinions by others. It isn't always possible to represent the viewpoints by clean separation, in advance, of examples representing each perspective and train a separate model for each point of view. In this thesis, we introduce lensing, a mixed-initiative technique to (i) extract lenses or mappings between machine-learned representations and perspectives of human experts, and (2) generate lensed models that afford multiple perspectives of the same dataset. We explore lensing of latent variable model in their configuration, parameter and evidential spaces. We apply lensing to three health applications, namely imbuing the perspectives of experts into latent variable models that analyze adolescent distress and crisis counseling.<br>by Karthik Dinakar.<br>Ph. D.
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Tebbifakhr, Amirhossein. "Machine Translation For Machines." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.
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Traditionally, Machine Translation (MT) systems are developed by targeting fluency (i.e. output grammaticality) and adequacy (i.e. semantic equivalence with the source text) criteria that reflect the needs of human end-users. However, recent advancements in Natural Language Processing (NLP) and the introduction of NLP tools in commercial services have opened new opportunities for MT. A particularly relevant one is related to the application of NLP technologies in low-resource language settings, for which the paucity of training data reduces the possibility to train reliable services. In this specific condition, MT can come into play by enabling the so-called “translation-based” workarounds. The idea is simple: first, input texts in the low-resource language are translated into a resource-rich target language; then, the machine-translated text is processed by well-trained NLP tools in the target language; finally, the output of these downstream components is projected back to the source language. This results in a new scenario, in which the end-user of MT technology is no longer a human but another machine. We hypothesize that current MT training approaches are not the optimal ones for this setting, in which the objective is to maximize the performance of a downstream tool fed with machine-translated text rather than human comprehension. Under this hypothesis, this thesis introduces a new research paradigm, which we named “MT for machines”, addressing a number of questions that raise from this novel view of the MT problem. Are there different quality criteria for humans and machines? What makes a good translation from the machine standpoint? What are the trade-offs between the two notions of quality? How to pursue machine-oriented objectives? How to serve different downstream components with a single MT system? How to exploit knowledge transfer to operate in different language settings with a single MT system? Elaborating on these questions, this thesis: i) introduces a novel and challenging MT paradigm, ii) proposes an effective method based on Reinforcement Learning analysing its possible variants, iii) extends the proposed method to multitask and multilingual settings so as to serve different downstream applications and languages with a single MT system, iv) studies the trade-off between machine-oriented and human-oriented criteria, and v) discusses the successful application of the approach in two real-world scenarios.
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Roderus, Jens, Simon Larson, and Eric Pihl. "Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.
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The amount of available data has allowed the field of machine learning to flourish. But with growing data set sizes comes an increase in algorithm execution times. Cluster computing frameworks provide tools for distributing data and processing power on several computer nodes and allows for algorithms to run in feasible time frames when data sets are large. Different cluster computing frameworks come with different trade-offs. In this thesis, the scalability of the execution time of machine learning algorithms running on the Hadoop cluster computing framework is investigated. A recent version of Hadoop and algorithms relevant in industry machine learning, namely K-means, latent Dirichlet allocation and naive Bayes are used in the experiments. This paper provides valuable information to anyone choosing between different cluster computing frameworks. The results show everything from moderate scalability to no scalability at all. These results indicate that Hadoop as a framework may have serious restrictions in how well tasks are actually parallelized. Possible scalability improvements could be achieved by modifying the machine learning library algorithms or by Hadoop parameter tuning.
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Collazo, Santiago Bryan Omar. "Machine learning blocks." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100301.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references.<br>This work presents MLBlocks, a machine learning system that lets data scientists explore the space of modeling techniques in a very easy and efficient manner. We show how the system is very general in the sense that virtually any problem and dataset can be casted to use MLBlocks, and how it supports the exploration of Discriminative Modeling, Generative Modeling and the use of synthetic features to boost performance. MLBlocks is highly parameterizable, and some of its powerful features include the ease of formulating lead and lag experiments for time series data, its simple interface for automation, and its extensibility to additional modeling techniques. We show how we used MLBlocks to quickly get results for two very different realworld data science problems. In the first, we used time series data from Massive Open Online Courses to cast many lead and lag formulations of predicting student dropout. In the second, we used MLBlocks' Discriminative Modeling functionality to find the best-performing model for predicting the destination of a car given its past trajectories. This later functionality is self-optimizing and will find the best model by exploring a space of 11 classification algorithms with a combination of Multi-Armed Bandit strategies and Gaussian Process optimizations, all in a distributed fashion in the cloud.<br>by Bryan Omar Collazo Santiago.<br>M. Eng.
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Shukla, Ritesh. "Machine learning ecosystem : implications for business strategy centered on machine learning." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/107342.
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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, System Design and Management Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 48-50).<br>As interest for adopting machine learning as a core component of a business strategy increases, business owners face the challenge of integrating an uncertain and rapidly evolving technology into their organization, and depending on this for the success of their strategy. The field of Machine learning has a rich set of literature for modeling of technical systems that implement machine learning. This thesis attempts to connect the literature for business and technology and for evolution and adoption of technology to the emergent properties of machine learning systems. This thesis provides high-level levers and frameworks to better prepare business owners to adopt machine learning to satisfy their strategic goals.<br>by Ritesh Shukla.<br>S.M. in Engineering and Management
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Huembeli, Patrick. "Machine learning for quantum physics and quantum physics for machine learning." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672085.
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Research at the intersection of machine learning (ML) and quantum physics is a recent growing field due to the enormous expectations and the success of both fields. ML is arguably one of the most promising technologies that has and will continue to disrupt many aspects of our lives. The way we do research is almost certainly no exception and ML, with its unprecedented ability to find hidden patterns in data, will be assisting future scientific discoveries. Quantum physics on the other side, even though it is sometimes not entirely intuitive, is one of the most successful physical theories and we are on the verge of adopting some quantum technologies in our daily life. Quantum many-body physics is a subfield of quantum physics where we study the collective behavior of particles or atoms and the emergence of phenomena that are due to this collective behavior, such as phases of matter. The study of phase transitions of these systems often requires some intuition of how we can quantify the order parameter of a phase. ML algorithms can imitate something similar to intuition by inferring knowledge from example data. They can, therefore, discover patterns that are invisible to the human eye, which makes them excellent candidates to study phase transitions. At the same time, quantum devices are known to be able to perform some computational task exponentially faster than classical computers and they are able to produce data patterns that are hard to simulate on classical computers. Therefore, there is the hope that ML algorithms run on quantum devices show an advantage over their classical analog.
This thesis is devoted to study two different paths along the front lines of ML and quantum physics. On one side, we study the use of neural networks (NN) to classify phases of mater in many-body quantum systems. On the other side, we study ML algorithms that run on quantum computers. The connection between ML for quantum physics and quantum physics for ML in this thesis is an emerging subfield in ML, the interpretability of learning algorithms. A crucial ingredient in the study of phase transitions with NNs is a better understanding of the predictions of the NN, to eventually infer a model of the quantum system and interpretability can assist us in this endeavor. The interpretability method that we study analyzes the influence of the training points on a test prediction and it depends on the curvature of the NN loss landscape. This further inspired an in-depth study of the loss of quantum machine learning (QML) applications which we as well will discuss.
In this thesis, we give answers to the questions of how we can leverage NNs to classify phases of matter and we use a method that allows to do domain adaptation to transfer the learned "intuition" from systems without noise onto systems with noise. To map the phase diagram of quantum many-body systems in a fully unsupervised manner, we study a method known from anomaly detection that allows us to reduce the human input to a mini mum. We will as well use interpretability methods to study NNs that are trained to distinguish phases of matter to understand if the NNs are learning something similar to an order parameter and if their way of learning can be made more accessible to humans. And finally, inspired by the interpretability of classical NNs, we develop tools to study the loss landscapes of variational quantum circuits to identify possible differences between classical and quantum ML algorithms that might be leveraged for a quantum advantage.<br>La investigación en la intersección del aprendizaje automático (machine learning, ML) y la física cuántica es una área en crecimiento reciente debido al éxito y las enormes expectativas de ambas áreas. ML es posiblemente una de las tecnologías más prometedoras que ha alterado y seguirá alterando muchos aspectos de nuestras vidas. Es casi seguro que la forma en que investigamos no es una excepción y el ML, con su capacidad sin precedentes para encontrar patrones ocultos en los datos ayudará a futuros descubrimientos científicos. La física cuántica, por otro lado, aunque a veces no es del todo intuitiva, es una de las teorías físicas más exitosas, y además estamos a punto de adoptar algunas tecnologías cuánticas en nuestra vida diaria. La física cuántica de los muchos cuerpos (many-body) es una subárea de la física cuántica donde estudiamos el comportamiento colectivo de partículas o átomos y la aparición de fenómenos que se deben a este comportamiento colectivo, como las fases de la materia. El estudio de las transiciones de fase de estos sistemas a menudo requiere cierta intuición de cómo podemos cuantificar el parámetro de orden de una fase. Los algoritmos de ML pueden imitar algo similar a la intuición al inferir conocimientos a partir de datos de ejemplo. Por lo tanto, pueden descubrir patrones que son invisibles para el ojo humano, lo que los convierte en excelentes candidatos para estudiar las transiciones de fase. Al mismo tiempo, se sabe que los dispositivos cuánticos pueden realizar algunas tareas computacionales exponencialmente más rápido que los ordenadores clásicos y pueden producir patrones de datos que son difíciles de simular en los ordenadores clásicos. Por lo tanto, existe la esperanza de que los algoritmos ML que se ejecutan en dispositivos cuánticos muestren una ventaja sobre su analógico clásico. Estudiamos dos caminos diferentes a lo largo de la vanguardia del ML y la física cuántica. Por un lado, estudiamos el uso de redes neuronales (neural network, NN) para clasificar las fases de la materia en sistemas cuánticos de muchos cuerpos. Por otro lado, estudiamos los algoritmos ML que se ejecutan en ordenadores cuánticos. La conexión entre ML para la física cuántica y la física cuántica para ML en esta tesis es un subárea emergente en ML: la interpretabilidad de los algoritmos de aprendizaje. Un ingrediente crucial en el estudio de las transiciones de fase con NN es una mejor comprensión de las predicciones de la NN, para inferir un modelo del sistema cuántico. Así pues, la interpretabilidad de la NN puede ayudarnos en este esfuerzo. El estudio de la interpretabilitad inspiró además un estudio en profundidad de la pérdida de aplicaciones de aprendizaje automático cuántico (quantum machine learning, QML) que también discutiremos. En esta tesis damos respuesta a las preguntas de cómo podemos aprovechar las NN para clasificar las fases de la materia y utilizamos un método que permite hacer una adaptación de dominio para transferir la "intuición" aprendida de sistemas sin ruido a sistemas con ruido. Para mapear el diagrama de fase de los sistemas cuánticos de muchos cuerpos de una manera totalmente no supervisada, estudiamos un método conocido de detección de anomalías que nos permite reducir la entrada humana al mínimo. También usaremos métodos de interpretabilidad para estudiar las NN que están entrenadas para distinguir fases de la materia para comprender si las NN están aprendiendo algo similar a un parámetro de orden y si su forma de aprendizaje puede ser más accesible para los humanos. Y finalmente, inspirados por la interpretabilidad de las NN clásicas, desarrollamos herramientas para estudiar los paisajes de pérdida de los circuitos cuánticos variacionales para identificar posibles diferencias entre los algoritmos ML clásicos y cuánticos que podrían aprovecharse para obtener una ventaja cuántica.
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Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
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Nella presente tesi di laurea viene preso in considerazione l’algoritmo di classificazione Support Vector Machine. Piu` in particolare si considera la sua formulazione come problema di ottimizazione Mixed Integer Program per la classificazione binaria super- visionata di un set di dati.
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Kent, W. F. "Machine learning for parameter identification of electric induction machines." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399178.
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This thesis is concerned with the application of simulated evolution (SE) to the steady-state parameter identification problem of a simulated and real 3-phase induction machine, over the no-load direct-on-line start period. In the case of the simulated 3-phase induction machine, the Kron's two-axis dynamic mathematical model was used to generate the real and simulated system responses where the induction machine parameters remain constant over the entire range of slip. The model was used in the actual value as well as the per-unit system, and the parameters were estimated using both the genetic algorithm (GA) and the evolutionary programming (EP) from the machine's dynamic response to a direct-on-line start. Two measurement vectors represented the dynamic responses and all the parameter identification processes were subject to five different levels of measurement noise. For the case of the real 3-phase induction machine, the real system responses were generated by the real 3-phase induction machine whilst the simulated system responses were generated by the Kron's model. However, the real induction machine's parameters are not constant over the range of slip, because of the nonlinearities caused by the skin effect and saturation. Therefore, the parameter identification of a real3-phase induction machine, using EP from the machine's dynamic response to a direct-on-line start, was not possible by applying the same methodology used for estimating the parameters of the simulated, constant parameters, 3-phase induction machine.
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Menke, Joshua E. "Improving machine learning through oracle learning /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1726.pdf.
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Menke, Joshua Ephraim. "Improving Machine Learning Through Oracle Learning." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/843.
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The following dissertation presents a new paradigm for improving the training of machine learning algorithms, oracle learning. The main idea in oracle learning is that instead of training directly on a set of data, a learning model is trained to approximate a given oracle's behavior on a set of data. This can be beneficial in situations where it is easier to obtain an oracle than it is to use it at application time. It is shown that oracle learning can be applied to more effectively reduce the size of artificial neural networks, to more efficiently take advantage of domain experts by approximating them, and to adapt a problem more effectively to a machine learning algorithm.
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Mauricio, Palacio Sebastián. "Machine-Learning Applied Methods." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/669286.
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The presented discourse followed several topics where every new chapter introduced an economic prediction problem and showed how traditional approaches can be complemented with new techniques like machine learning and deep learning. These powerful tools combined with principles of economic theory is highly increasing the scope for empiricists. Chapter 3 addressed this discussion. By progressively moving from Ordinary Least Squares, Penalized Linear Regressions and Binary Trees to advanced ensemble trees. Results showed that ML algorithms significantly outperform statistical models in terms of predictive accuracy. Specifically, ML models perform 49-100\% better than unbiased methods. However, we cannot rely on parameter estimations. For example, Chapter 4 introduced a net prediction problem regarding fraudulent property claims in insurance. Despite the fact that we got extraordinary results in terms of predictive power, the complexity of the problem restricted us from getting behavioral insight. Contrarily, statistical models are easily interpretable. Coefficients give us the sign, the magnitude and the statistical significance. We can learn behavior from marginal impacts and elasticities. Chapter 5 analyzed another prediction problem in the insurance market, particularly, how the combination of self-reported data and risk categorization could improve the detection of risky potential customers in insurance markets. Results were also quite impressive in terms of prediction, but again, we did not know anything about the direction or the magnitude of the features. However, by using a Probit model, we showed the benefits of combining statistic models with ML-DL models. The Probit model let us get generalizable insights on what type of customers are likely to misreport, enhancing our results. Likewise, Chapter 2 is a clear example of how causal inference can benefit from ML and DL methods. These techniques allowed us to capture that 70 days before each auction there were abnormal behaviors in daily prices. By doing so, we could apply a solid statistical model and we could estimate precisely what the net effect of the mandated auctions in Spain was. This thesis aims at combining advantages of both methodologies, machine learning and econometrics, boosting their strengths and attenuating their weaknesses. Thus, we used ML and statistical methods side by side, exploring predictive performance and interpretability.
Several conditions can be inferred from the nature of both approaches. First, as we have observed throughout the chapters, ML and traditional econometric approaches solve fundamentally different problems. We use ML and DL techniques to predict, not in terms of traditional forecast, but making our models generalizable to unseen data. On the other hand, traditional econometrics has been focused on causal inference and parameter estimation. Therefore, ML is not replacing traditional techniques, but rather complementing them. Second, ML methods focus in out-of-sample data instead of in-sample data, while statistical models typically focus on goodness-of-fit. It is then not surprising that ML techniques consistently outperformed traditional techniques in terms of predictive accuracy. The cost is then biased estimators. Third, the tradition in economics has been to choose a unique model based on theoretical principles and to fit the full dataset on it and, in consequence, obtaining unbiased estimators and their respective confidence intervals. On the other hand, ML relies on data driven selection models, and does not consider causal inference. Instead of manually choosing the covariates, the functional form is determined by the data. This also translates to the main weakness of ML, which is the lack of inference of the underlying data-generating process. I.e. we cannot derive economically meaningful conclusions from the coefficients. Focusing on out-of-sample performance comes at the expense of the ability to infer causal effects, due to the lack of standard errors on the coefficients. Therefore, predictors are typically biased, and estimators may not be normally distributed. Thus, we can conclude that in terms of out-sample performance it is hard to compete against ML models. However, ML cannot contend with the powerful insights that the causal inference analysis gives us, which allow us not only to get the most important variables and their magnitude but also the ability to understand economic behaviors.
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Pace, Aaron J. "Guided Interactive Machine Learning." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1355.pdf.
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Montanez, George D. "Why Machine Learning Works." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1114.
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To better understand why machine learning works, we cast learning problems as searches and characterize what makes searches successful. We prove that any search algorithm can only perform well on a narrow subset of problems, and show the effects of dependence on raising the probability of success for searches. We examine two popular ways of understanding what makes machine learning work, empirical risk minimization and compression, and show how they fit within our search frame-work. Leveraging the “dependence-first” view of learning, we apply this knowledge to areas of unsupervised time-series segmentation and automated hyperparameter optimization, developing new algorithms with strong empirical performance on real-world problem classes.
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Thomaz, Andrea Lockerd. "Socially guided machine learning." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36160.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.<br>Includes bibliographical references (p. 139-146).<br>Social interaction will be key to enabling robots and machines in general to learn new tasks from ordinary people (not experts in robotics or machine learning). Everyday people who need to teach their machines new things will find it natural for to rely on their interpersonal interaction skills. This thesis provides several contributions towards the understanding of this Socially Guided Machine Learning scenario. While the topic of human input to machine learning algorithms has been explored to some extent, prior works have not gone far enough to understand what people will try to communicate when teaching a machine and how algorithms and learning systems can be modified to better accommodate a human partner. Interface techniques have been based on intuition and assumptions rather than grounded in human behavior, and often techniques are not demonstrated or evaluated with everyday people. Using a computer game, Sophie's Kitchen, an experiment with human subjects provides several insights about how people approach the task of teaching a machine. In particular, people want to direct and guide an agent's exploration process, they quickly use the behavior of the agent to infer a mental model of the learning process, and they utilize positive and negative feedback in asymmetric ways.<br>(cont.) Using a robotic platform, Leonardo, and 200 people in follow-up studies of modified versions of the Sophie's Kitchen game, four research themes are developed. The use of human guidance in a machine learning exploration can be successfully incorporated to improve learning performance. Novel learning approaches demonstrate aspects of goal-oriented learning. The transparency of the machine learner can have significant effects on the nature of the instruction received from the human teacher, which in turn positively impacts the learning process. Utilizing asymmetric interpretations of positive and negative feedback from a human partner, can result in a more efficient and robust learning experience.<br>by Andrea Lockerd Thomaz.<br>Ph.D.
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Leather, Hugh. "Machine learning in compilers." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/9810.
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Tuning a compiler so that it produces optimised code is a difficult task because modern processors are complicated; they have a large number of components operating in parallel and each is sensitive to the behaviour of the others. Building analytical models on which optimisation heuristics can be based has become harder as processor complexity increased and this trend is bound to continue as the world moves towards further heterogeneous parallelism. Compiler writers need to spend months to get a heuristic right for any particular architecture and these days compilers often support a wide range of disparate devices. Whenever a new processor comes out, even if derived from a previous one, the compiler’s heuristics will need to be retuned for it. This is, typically, too much effort and so, in fact, most compilers are out of date. Machine learning has been shown to help; by running example programs, compiled in different ways, and observing how those ways effect program run-time, automatic machine learning tools can predict good settings with which to compile new, as yet unseen programs. The field is nascent, but has demonstrated significant results already and promises a day when compilers will be tuned for new hardware without the need for months of compiler experts’ time. Many hurdles still remain, however, and while experts no longer have to worry about the details of heuristic parameters, they must spend their time on the details of the machine learning process instead to get the full benefits of the approach. This thesis aims to remove some of the aspects of machine learning based compilers for which human experts are still required, paving the way for a completely automatic, retuning compiler. First, we tackle the most conspicuous area of human involvement; feature generation. In all previous machine learning works for compilers, the features, which describe the important aspects of each example to the machine learning tools, must be constructed by an expert. Should that expert choose features poorly, they will miss crucial information without which the machine learning algorithm can never excel. We show that not only can we automatically derive good features, but that these features out perform those of human experts. We demonstrate our approach on loop unrolling, and find we do better than previous work, obtaining XXX% of the available performance, more than the XXX% of previous state of the art. Next, we demonstrate a new method to efficiently capture the raw data needed for machine learning tasks. The iterative compilation on which machine learning in compilers depends is typically time consuming, often requiring months of compute time. The underlying processes are also noisy, so that most prior works fall into two categories; those which attempt to gather clean data by executing a large number of times and those which ignore the statistical validity of their data to keep experiment times feasible. Our approach, on the other hand guarantees clean data while adapting to the experiment at hand, needing an order of magnitude less work that prior techniques.
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Armani, Luca. "Machine Learning: Customer Segmentation." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24925/.
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Con lo scopo di risparmiare capitale e incrementare i profitti tramite attività di marketing sempre più mirate, conoscere le preferenze di un cliente e supportarlo nell’acquisto, sta passando dall’essere una scelta all’essere una necessità. A tal proposito, le aziende si stanno muovendo verso un approccio
sempre più automatizzato per riuscire a classificare la clientela, cos`ı da ottimizzare sempre più l’esperienza d’acquisto. Tramite il Machine Learning è possibile effettuare svariati tipi di analisi che consentano di raggiungere questo scopo.
L’obiettivo di questo progetto è, in prima fase, di dare una panoramica al lettore su quali siano le tecniche e gli strumenti che mette a disposizione il ML. In un secondo momento verrà descritto il problema della Customer Segmentation e quali tecniche e benefici porta con sé questo tema. Per finire,
verranno descritte le varie fasi su cui si fonda il seguente progetto di ML rivolto alla classificazione della clientela, basandosi sul totale di spesa effettuata in termini monetari e la quantità di articoli acquistati.
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Du, Buisson Lise. "Machine learning in astronomy." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15502.
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The search to find answers to the deepest questions we have about the Universe has fueled the collection of data for ever larger volumes of our cosmos. The field of supernova cosmology, for example, is seeing continuous development with upcoming surveys set to produce a vast amount of data that will require new statistical inference and machine learning techniques for processing and analysis. Distinguishing between real objects and artefacts is one of the first steps in any transient science pipeline and, currently, is still carried out by humans - often leading to hand scanners having to sort hundreds or thousands of images per night. This is a time-consuming activity introducing human biases that are extremely hard to characterise. To succeed in the objectives of future transient surveys, the successful substitution of human hand scanners with machine learning techniques for the purpose of this artefact-transient classification therefore represents a vital frontier. In this thesis we test various machine learning algorithms and show that many of them can match the human hand scanner performance in classifying transient difference g, r and i-band imaging data from the SDSS-II SN Survey into real objects and artefacts. Using principal component analysis and linear discriminant analysis, we construct a grand total of 56 feature sets with which to train, optimise and test a Minimum Error Classifier (MEC), a naive Bayes classifier, a k-Nearest Neighbours (kNN) algorithm, a Support Vector Machine (SVM) and the SkyNet artificial neural network.
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Punugu, Venkatapavani Pallavi. "Machine Learning in Neuroimaging." Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10284048.
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<p> The application of machine learning algorithms to analyze and determine disease related patterns in neuroimaging has emerged to be of extreme interest in Computer-Aided Diagnosis (CAD). This study is a small step towards categorizing Alzheimer's disease, Neurode-generative diseases, Psychiatric diseases and Cerebrovascular Small Vessel diseases using CAD. In this study, the SPECT neuroimages are pre-processed using powerful data reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA) and Automated Anatomical Labeling (AAL). Each of the pre-processing methods is used in three machine learning algorithms namely: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and k-Nearest Neighbors (k-nn) to recognize disease patterns and classify the diseases. While neurodegenerative diseases and psychiatric diseases overlap with a mix of diseases and resulted in fairly moderate classification, the classification between Alzheimer's disease and Cerebrovascular Small Vessel diseases yielded good results with an accuracy of up to 73.7%.</p><p>
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Lounici, Sofiane. "Watermarking machine learning models." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS282.pdf.
Full textAbstract:
La protection de la propriété intellectuelle des modèles d’apprentissage automatique apparaît de plus en plus nécessaire, au vu des investissements et de leur impact sur la société. Dans cette thèse, nous proposons d’étudier le tatouage de modèles d’apprentissage automatique. Nous fournissons un état de l’art sur les techniques de tatouage actuelles, puis nous le complétons en considérant le tatouage de modèles au-delà des tâches de classification d’images. Nous définissons ensuite les attaques de contrefaçon contre le tatouage pour les plateformes d’hébergement de modèles, et nous présentons une nouvelle technique de tatouages par biais algorithmique. De plus, nous proposons une implémentation des techniques présentées<br>The protection of the intellectual property of machine learning models appears to be increasingly necessary, given the investments and their impact on society. In this thesis, we propose to study the watermarking of machine learning models. We provide a state of the art on current watermarking techniques, and then complement it by considering watermarking beyond image classification tasks. We then define forging attacks against watermarking for model hosting platforms and present a new fairness-based watermarking technique. In addition, we propose an implementation of the presented techniques
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Thorén, Daniel. "Radar based tank level measurement using machine learning : Agricultural machines." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176259.
Full textAbstract:
Agriculture is becoming more dependent on computerized solutions to make thefarmer’s job easier. The big step that many companies are working towards is fullyautonomous vehicles that work the fields. To that end, the equipment fitted to saidvehicles must also adapt and become autonomous. Making this equipment autonomoustakes many incremental steps, one of which is developing an accurate and reliable tanklevel measurement system. In this thesis, a system for tank level measurement in a seedplanting machine is evaluated. Traditional systems use load cells to measure the weightof the tank however, these types of systems are expensive to build and cumbersome torepair. They also add a lot of weight to the equipment which increases the fuel consump-tion of the tractor. Thus, this thesis investigates the use of radar sensors together witha number of Machine Learning algorithms. Fourteen radar sensors are fitted to a tankat different positions, data is collected, and a preprocessing method is developed. Then,the data is used to test the following Machine Learning algorithms: Bagged RegressionTrees (BG), Random Forest Regression (RF), Boosted Regression Trees (BRT), LinearRegression (LR), Linear Support Vector Machine (L-SVM), Multi-Layer Perceptron Re-gressor (MLPR). The model with the best 5-fold crossvalidation scores was Random For-est, closely followed by Boosted Regression Trees. A robustness test, using 5 previouslyunseen scenarios, revealed that the Boosted Regression Trees model was the most robust.The radar position analysis showed that 6 sensors together with the MLPR model gavethe best RMSE scores.In conclusion, the models performed well on this type of system which shows thatthey might be a competitive alternative to load cell based systems.
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Romano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.
Full textAbstract:
In questo lavoro di tesi è stato analizzato l'avvento dell'industria 4.0 all'interno dell' industria nel settore packaging. In particolare, è stata discussa l'importanza della diagnostica predittiva e sono stati analizzati e testati diversi approcci per la determinazione di modelli descrittivi del problema a partire dai dati. Inoltre, sono state applicate le principali tecniche di Machine Learning in modo da classificare i dati analizzati nelle varie classi di appartenenza.
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Schneider, C. "Using unsupervised machine learning for fault identification in virtual machines." Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.
Full textAbstract:
Self-healing systems promise operating cost reductions in large-scale computing environments through the automated detection of, and recovery from, faults. However, at present there appears to be little known empirical evidence comparing the different approaches, or demonstrations that such implementations reduce costs. This thesis compares previous and current self-healing approaches before demonstrating a new, unsupervised approach that combines artificial neural networks with performance tests to perform fault identification in an automated fashion, i.e. the correct and accurate determination of which computer features are associated with a given performance test failure. Several key contributions are made in the course of this research including an analysis of the different types of self-healing approaches based on their contextual use, a baseline for future comparisons between self-healing frameworks that use artificial neural networks, and a successful, automated fault identification in cloud infrastructure, and more specifically virtual machines. This approach uses three established machine learning techniques: Naïve Bayes, Baum-Welch, and Contrastive Divergence Learning. The latter demonstrates minimisation of human-interaction beyond previous implementations by producing a list in decreasing order of likelihood of potential root causes (i.e. fault hypotheses) which brings the state of the art one step closer toward fully self-healing systems. This thesis also examines the impact of that different types of faults have on their respective identification. This helps to understand the validity of the data being presented, and how the field is progressing, whilst examining the differences in impact to identification between emulated thread crashes and errant user changes – a contribution believed to be unique to this research. Lastly, future research avenues and conclusions in automated fault identification are described along with lessons learned throughout this endeavor. This includes the progression of artificial neural networks, how learning algorithms are being developed and understood, and possibilities for automatically generating feature locality data.
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Sow, Souleymane. "Simulateur vibratoire de machines tournantes à base de machine-learning." Electronic Thesis or Diss., Reims, 2024. http://www.theses.fr/2024REIMS038.
Full textAbstract:
Placée dans le cadre de la maintenance 4.0, cette thèse vise à développer un simulateur vibratoire pour un banc d’essais de roulements à gorges profondes, afin d’améliorer la précision des diagnostics basés sur des algorithmes de machine learning. Le travail se concentre d’abord sur l’intégration de la flexibilité des paliers dans un modèle numérique existant, créant ainsi un jumeau numérique plus représentatif des conditions réelles d’exploitation. La méthodologie s’articule autour de la conception d’un modèle numérique hybride, combinant un modèle méso (éléments discrets) et un modèle macro (éléments finis). Ces deux sous-modèles communiquent pour décrire la dynamique du système et simuler différents modes de fonctionnement. Les données générées par le simulateur sont ensuite exploitées pour mettre à jour le modèle et entraîner des algorithmes de classification (MSVM, KNN, arbres de décision), obtenant un taux de classification de 94 %. Ce résultat montre une amélioration de 10 % par rapport aux méthodes précédentes, confirmant l’efficacité de l’approche. Dans le but de mesurer l’apport qualitatif et quantitatif des données dans un diagnostic par classification, les données issues de ce modèle numérique mis à jour sont utilisées dans différentes stratégies d’hybridation de données avec les celles mesurées sur le système<br>Framed within maintenance 4.0, this thesis aims to develop a vibratory simulator for a deep-groove ball bearing test bench to improve the accuracy of diagnostics based on machine learning algorithms. The work initially focuses on integrating the flexibility of the bearing supports into an existing numerical model, thereby creating a digital twin that more accurately reflects real operating conditions. The methodology is centred around the design of a hybrid numerical model, combining a meso-model (discrete elements) and a macro-model (finite elements). These two sub-models interact to describe the system's dynamics and simulate different operating modes. The data generated by the simulator is then used to update the model and train classification algorithms (MSVM, KNN, decision trees), achieving a classification accuracy of 94%. This result demonstrates a 10% improvement compared to previous methods, confirming the approach’s effectiveness. To assess the qualitative and quantitative contributions of the data in a classification-based diagnostic, the data from the updated numerical model is used in various data hybridization strategies with those measured from the physical system
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Johansson, Richard. "Machine learning på tidsseriedataset : En utvärdering av modeller i Azure Machine Learning Studio." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71223.
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In line with technology advancements in processing power and storing capabilities through cloud services, higher demands are set on companies’ data sets. Business executives are now expecting analyses of real time data or massive data sets, where traditional Business Intelligence struggle to deliver. The interest of using machine learning to predict trends and patterns which the human eye can’t see is thus higher than ever. Time series data sets are data sets characterised by a time stamp and a value; for example, a sensor data set. The company with which I’ve been in touch collects data from sensors in a control room. In order to predict patterns and in the future using these in combination with other data, the company wants to apply machine learning on their data set. To do this effectively, the right machine learning model needs to be selected. This thesis therefore has the purpose of finding out which machine learning model, or models, from the selected platform – Azure Machine Learning Studio – works best on a time series data set with sensor data. The models are then tested through a machine learning pilot on the company’s data Throughout the thesis, multiple machine learning models from the selected platform are evaluated. For the data set in hand, the conclusion is that a supervised regression model by the type of a Decision Forest Regression model gives the best results and has the best chance to adapt to a data set growing in size. Another conclusion is that more training data is needed to give the model an even better result, especially since it’s taking date and week day into account. Adjustments of the parameters for each model might also affect the result, opening up for further improvements.
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Wu, Anjian M. B. A. Sloan School of Management. "Performance modeling of human-machine interfaces using machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122599.
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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019, In conjunction with the Leaders for Global Operations Program at MIT<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 70-71).<br>As the popularity of online retail expands, world-class electronic commerce (e-commerce) businesses are increasingly adopting collaborative robotics and Internet of Things (IoT) technologies to enhance fulfillment efficiency and operational advantage. E-commerce giants like Alibaba and Amazon are known to have smart warehouses staffed by both machines and human operators. The robotics systems specialize in transporting and maneuvering heavy shelves of goods to and from operators. Operators are left to higher-level cognitive tasks needed to process goods such as identification and complex manipulation of individual objects. Achieving high system throughput in these systems require harmonized interaction between humans and machines. The robotics systems must minimize time that operators are waiting for new work (idle time) and operators need to minimize time processing items (takt time). Over time, these systems will naturally generate extensive amounts of data. Our research provides insights into both using this data to design a machine-learning (ML) model of takt time, as well as exploring methods of interpreting insights from such a model. We start by presenting our iterative approach to developing a ML model that predicts the average takt of a group of operators at hourly intervals. Our final XGBoost model reached an out-of-sample performance of 4.01% mean absolute percent error (MAPE) using over 250,000 hours of historic data across multiple warehouses around the world. Our research will share methods to cross-examine and interpret the relationships learned by the model for business value. This can allow organizations to effectively quantify system trade-offs as well as identify root-causes of takt performance deviations. Finally, we will discuss the implications of our empirical findings.<br>by Anjian Wu.<br>M.B.A.<br>S.M.<br>M.B.A. Massachusetts Institute of Technology, Sloan School of Management<br>S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Ohlsson, Caroline. "Exploring the potential of machine learning : How machine learning can support financial risk management." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324684.
Full textAbstract:
For decades, there have been developments of computer software to support human decision making. Along with the increased complexity of business environments, smart technologies are becoming popular and useful for decision support based on huge amount of information and advanced analysis. The aim of this study was to explore the potential of using machine learning for financial risk management in debt collection, with a purpose of providing a clear description of what possibilities and difficulties there are. The exploration was done from a business perspective in order to complement previous research using a computer science approach which centralizes on the development and testing of algorithms. By conducting a case study at Tieto, who provides a market leading debt collection system, data was collected about the process and the findings were analyzed based on machine learning theories. The results showed that machine learning has the potential to improve the predictions for risk assessment through advanced pattern recognition and adapting to changes in the environment. Furthermore, it also has the potential to provide the decision maker with customized suggestions for suitable risk mitigation strategies based on experiences and evaluations of previous strategic decisions. However, the issues related to data availability were concluded as potential difficulties due to the limitations of accessing more data from authorities through an automated process. Moreover, the potential is highly dependent on future laws and regulations for data management which will affect the difficulty of data availability further.
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VELLOSO, SUSANA ROSICH SOARES. "SQLLOMINING: FINDING LEARNING OBJECTS USING MACHINE LEARNING METHODS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10970@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Objetos de Aprendizagem ou Learning Objects (LOs) são
porções de
material didático tais como textos que podem ser
reutilizados na composição de
outros objetos maiores (aulas ou cursos). Um dos problemas
da reutilização de
LOs é descobri-los em seus contextos ou documentos texto
originais tais como
livros, e artigos. Visando a obtenção de LOs, este
trabalho apresenta um
processo que parte da extração, tratamento e carga de uma
base de dados
textual e em seguida, baseando-se em técnicas de
aprendizado de máquina,
uma combinação de EM (Expectation-Maximization) e um
classificador
Bayesiano, classifica-se os textos extraídos. Tal processo
foi implementado em
um sistema chamado SQLLOMining, que usa SQL como linguagem
de
programação e técnicas de mineração de texto na busca de
LOs.<br>Learning Objects (LOs) are pieces of instructional
material like traditional
texts that can be reused in the composition of more
complex objects like classes
or courses. There are some difficulties in the process of
LO reutilization. One of
them is to find pieces of documents that can be used like
LOs. In this work we
present a process that, in search for LOs, starts by
extracting, transforming and
loading a text database and then continue clustering these
texts, using a machine
learning methods that combines EM (Expectation-
Maximization) and a Bayesian
classifier. We implemented that process in a system called
SQLLOMining that
uses the SQL language and text mining methods in the
search for LOs.
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Grangier, David. "Machine learning for information retrieval." Lausanne : École polytechnique fédérale de Lausanne, 2008. http://aleph.unisg.ch/volltext/464553_Grangier_Machine_learning_for_information_retrieval.pdf.
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Baglioni, Cecilia. "Processi Gaussiani e Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20704/.
Full textAbstract:
In questa tesi si approfondisce la teoria dei Processi Gaussiani utilizzando i modelli di regressione per arrivare alla distribuzione predittiva a posteriori e studiandone le proprietà di differenziabilità per le Greche: in questo senso si introduce il Machine Learning tramite la tecnica del Supervised Learning. Nel primo capitolo si elencano le ipotesi alla base del modello di Black e Scholes e il procedimento per ottenere la formula. Si arriva, così, alla definizione delle Greche utili a valutare la sensibilità del portafoglio rispetto alla variazione dei fattori da cui la formula di Black e Scholes dipende. Nel secondo capitolo, partendo da un modello di regressione lineare, si arriva alla distribuzione predittiva a posteriori. Per superare la limitatezza del modello lineare Bayesiano si introduce un nuovo modello di regressione che conserva la linearità rispetto ai parametri e che viene applicato ad un nuovo spazio detto feature: si ottiene una nuova espressione per la distribuzione predittiva con un minor costo computazionale. Il modello in questione rappresenta un esempio di Processo Gaussiano con media e covarianza definite. Si arriva a trovare un collegamento tra le Greche e i processi Gaussiani: Delta e Gamma, infatti, sono calcolate a partire dalla derivata prima e seconda del processo in questione. Nel terzo capitolo, si applicano i risultati teorici a dati ottenuti con il modello di Black e Scholes. Si fa uso del pacchetto R DiceKriging con le funzioni km, predict e simulate. Il training set è composto
dal prezzo della Put, valori della Delta e prezzo del sottostante calcolati con la formula di Black e Scholes, il validation set da 300 osservazioni. Km fornisce la stima dei parametri di covarianza e di media. Predict e simulate restituiscono i valori della media e deviazione standard predetti e i valori degli output dell'insieme test.
Infine i metodi si confrontano calcolando gli errori, costo computazionale e memoria.
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Liao, Yihua. "Machine learning in intrusion detection /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2005. http://uclibs.org/PID/11984.
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Strobl, Carolin. "Statistical Issues in Machine Learning." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-89043.
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Stendahl, Jonas, and Johan Arnör. "Gesture Keyboard USING MACHINE LEARNING." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157141.
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The market for mobile devices is expanding rapidly. Input of text is a large part of using a mobile device and an input method that is convenient and fast is therefore very interesting. Gesture keyboards allow the user to input text by dragging a finger over the letters in the desired word. This study investigates if enhancements of gesture keyboards can be accomplished using machine learning. A gesture keyboard was developed based on an algorithm which used a Multilayer Perceptron with backpropagation and evaluated. The results indicate that the evaluated implementation is not an optimal solution to the problem of recognizing swiped words.<br>Marknaden för mobila enheter expanderar kraftigt. Inmatning är en viktig del vid användningen av sådana produkter och en inmatningsmetod som är smidig och snabb är därför mycket intressant. Ett tangentbord för gester erbjuder användaren möjligheten att skriva genom att dra fingret över bokstäverna i det önskade ordet. I denna studie undersöks om tangentbord för gester kan förbättras med hjälp av maskininlärning. Ett tangentbord som använde en Multilayer Perceptron med backpropagation utvecklades och utvärderades. Resultaten visar att den undersökta implementationen inte är en optimal lösning på problemet att känna igen ord som matas in med hjälp av gester.
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Bergkvist, Markus, and Tobias Olandersson. "Machine learning in simulated RoboCup." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3827.
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An implementation of the Electric Field Approach applied to the simulated RoboCup is presented, together with a demonstration of a learning system. Results are presented from the optimization of the Electric Field parameters in a limited situation, using the learning system. Learning techniques used in contemporary RoboCup research are also described including a brief presentation of their results.
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Stefan, Vasic, and Lindgren Nicklas. "Product categorisation using machine learning." Thesis, KTH, Data- och elektroteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209031.
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Machine learning is a method in data science for analysing large data sets and extracting hidden patterns and common characteristics in the data. Corporations often have access to databases containing great amounts of data that could contain valuable information. Navetti AB wants to investigate the possibility to automate their product categorisation by evaluating different types of machine learning algorithms. This could increase both time- and cost efficiency. This work resulted in three different prototypes, each using different machine learning algorithms with the ability to categorise products automatically. The prototypes were tested and evaluated based on their ability to categorise products and their performance in terms of speed. Different techniques used for preprocessing data is also evaluated and tested. An analysis of the tests shows that when providing a suitable algorithm with enough data it is possible to automate the manual categorisation.<br>Maskininlärning är en metod inom datavetenskap vars uppgift är att analysera stora mängder data och hitta dolda mönster och gemensamma karaktärsdrag. Företag har idag ofta tillgång till stora mängder data som i sin tur kan innehålla värdefull information. Navetti AB vill undersöka möjligheten att automatisera sin produktkategorisering genom att utvärdera olika typer av maskininlärnings- algoritmer. Detta skulle dramatiskt öka effektiviteten både tidsmässigt och ekonomiskt. Resultatet blev tre prototyper som implementerar tre olika maskininlärnings-algoritmer som automatiserat kategoriserar produkter. Prototyperna testades och utvärderades utifrån dess förmåga att kategorisera och dess prestanda i form av hastighet. Olika tekniker som används för att förbereda data analyseras och utvärderas. En analys av testerna visar att med tillräckligt mycket data och en passande algoritm så är det möjligt att automatisera den manuella kategoriseringen.
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Bhat, Sooraj. "Syntactic foundations for machine learning." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47700.
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Machine learning has risen in importance across science, engineering, and business in
recent years. Domain experts have begun to understand how their data analysis problems
can be solved in a principled and efficient manner using methods from machine learning,
with its simultaneous focus on statistical and computational concerns. Moreover, the data
in many of these application domains has exploded in availability and scale, further underscoring the need for algorithms which find patterns and trends quickly and correctly.
However, most people actually analyzing data today operate far from the expert level.
Available statistical libraries and even textbooks contain only a finite sample of the possibilities afforded by the underlying mathematical principles. Ideally, practitioners should
be able to do what machine learning experts can do--employ the fundamental principles to
experiment with the practically infinite number of possible customized statistical models as
well as alternative algorithms for solving them, including advanced techniques for handling
massive datasets. This would lead to more accurate models, the ability in some cases to
analyze data that was previously intractable, and, if the experimentation can be greatly
accelerated, huge gains in human productivity.
Fixing this state of affairs involves mechanizing and automating these statistical and
algorithmic principles. This task has received little attention because we lack a suitable
syntactic representation that is capable of specifying machine learning problems and solutions, so there is no way to encode the principles in question, which are themselves a
mapping between problem and solution. This work focuses on providing the foundational
layer for enabling this vision, with the thesis that such a representation is possible. We
demonstrate the thesis by defining a syntactic representation of machine learning that is
expressive, promotes correctness, and enables the mechanization of a wide variety of useful
solution principles.
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Parson, Rupert. "Machine learning of changing concepts." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393757.
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Swere, Erick A. R. "Machine learning in embedded systems." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/4969.
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This thesis describes novel machine learning techniques specifically designed for use in real-time embedded systems. The techniques directly address three major requirements of such learning systems. Firstly, learning must be capable of being achieved incrementally, since many applications do not have a representative training set available at the outset. Secondly, to guarantee real-time performance, the techniques must be able to operate within a deterministic and limited time bound. Thirdly, the memory requirement must be limited and known a priori to ensure the limited memory available to hold data in embedded systems will not be exceeded. The work described here has three principal contributions. The frequency table is a data structure specifically designed to reduce the memory requirements of incremental learning in embedded systems. The frequency table facilitates a compact representation of received data that is sufficient for decision tree generation. The frequency table decision tree (FTDT) learning method provides classification performance similar to existing decision tree approaches, but extends these to incremental learning while substantially reducing memory usage for practical problems. The incremental decision path (IDP) method is able to efficiently induce, from the frequency table of observations, the path through a decision tree that is necessary for the classification of a single instance. The classification performance of IDP is equivalent to that of existing decision tree algorithms, but since IDP allows the maximum number of partial decision tree nodes to be determined prior to the generation of the path, both the memory requirement and the execution time are deterministic. In this work, the viability of the techniques is demonstrated through application to realtime mobile robot navigation.
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林謀楷 and Mau-kai Lam. "Inductive machine learning with bias." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31212426.
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Pantziarka, P. "Machine learning and data validation." Thesis, University of Surrey, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425812.
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Friberg, Christin. "Analysing ConformationalEnsembles using Machine Learning." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231781.
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G-protein coupled receptors are involved in many diseases and act as target for severalpharmaceutical drugs. To develop methods to understand the dynamics of proteinsinvolved in the communication through the cell membrane is therefore crucial. In thisstudy, data from molecular dynamics trajectories of the !2-adrenergic receptor were studiedwith the aim to train a neural network to identify the receptor due to its bindingmechanism. Represented as conformational ensembles, the protein was examined to identifythe binding state of the receptor based on determinants of its conformation. Thethesis demonstrate that this conceptually simple method can be used to computationallyanalyze the molecular response on drug binding.<br>G-proteinkopplade receptorer är delaktiga i flertalet sjukdomar och agerar måltavla förett stort antal läkemedel. Att utveckla metoder för att bättre förstå dynamiken hos deproteiner som sköter kommunikationen över cellens membran är därför mycket viktigt. Idenna studie analyseras data från molekyldynamiska simuleringar av !2-adrenergena receptorermed syfte att låta ett neuralt nätverk lära sig identifiera receptorn med avseendepå dess bindningsmekanism. Proteinet studerades som konformationella ensembler föratt hitta receptorns olika bindningstillstånd. Studien visar att denna konceptuellt enklametod kan användas för att analysera den komplexa molekylära process som sker vidläkemedelsbindning.
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OGURI, PEDRO. "MACHINE LEARNING FOR SENTIMENT CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9947@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Sentiment Analysis é um problema de categorização de texto
no qual deseja-se identificar opiniões favoráveis e
desfavoráveis com relação a um tópico.
Um exemplo destes tópicos de interesse são organizações e
seus produtos. Neste problema, documentos são
classificados pelo sentimento, conotação,
atitudes e opiniões ao invés de se restringir aos fatos
descritos neste. O principal desafio em Sentiment
Classification é identificar como sentimentos são
expressados em textos e se tais sentimentos indicam uma
opinião positiva (favorável) ou negativa (desfavorável)
com relação a um tópico. Devido ao crescente volume de
dados disponível na Web, onde todos tendem
a ser geradores de conteúdo e expressarem opiniões sobre
os mais variados assuntos, técnicas de Aprendizado de
Máquina vem se tornando cada vez mais atraentes.
Nesta dissertação investigamos métodos de Aprendizado de
Máquina para Sentiment Analysis. Apresentamos alguns
modelos de representação de documentos como saco de
palavras e N-grama. Testamos os classificadores
SVM (Máquina de Vetores Suporte) e Naive Bayes com
diferentes modelos de representação textual e comparamos
seus desempenhos.<br>Sentiment Analysis is a text categorization problem in
which we want to
identify favorable and unfavorable opinions towards a
given topic. Examples
of such topics are organizations and its products. In this
problem, docu-
ments are classifed according to their sentiment,
connotation, attitudes and
opinions instead of being limited to the facts described
in it.
The main challenge in Sentiment Classification is
identifying how sentiments
are expressed in texts and whether they indicate a
positive (favorable) or
negative (unfavorable) opinion towards a topic. Due to the
growing volume
of information available online in an environment where we
all tend to be
content generators and express opinions on a variety of
subjects, Machine
Learning techniques have become more and more attractive.
In this dissertation, we investigate Machine Learning
methods applied to
Sentiment Analysis. We present document representation
models such as
bag-of-words and N-grams.We compare the performance of the
Naive Bayes
and the Support Vector Machine classifiers for each
proposed model
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Huang, Zongyan. "Machine learning and computer algebra." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709397.
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Ferreira, Paulo Victor Rodrigues. "SRML: Space Radio Machine Learning." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/199.
Full textAbstract:
Space-based communications systems to be employed by future artificial satellites, or spacecraft during exploration missions, can potentially benefit from software-defined radio adaptation capabilities. Multiple communication requirements could potentially compete for radio resources, whose availability of which may vary during the spacecraft's operational life span. Electronic components are prone to failure, and new instructions will eventually be received through software updates. Consequently, these changes may require a whole new set of near-optimal combination of parameters to be derived on-the-fly without instantaneous human interaction or even without a human in-the-loop. Thus, achieving a sufficiently set of radio parameters can be challenging, especially when the communication channels change dynamically due to orbital dynamics as well as atmospheric and space weather-related impairments. This dissertation presents an analysis and discussion regarding novel algorithms proposed in order to enable a cognition control layer for adaptive communication systems operating in space using an architecture that merges machine learning techniques employing wireless communication principles. The proposed cognitive engine proof-of-concept reasons over time through an efficient accumulated learning process. An implementation of the conceptual design is expected to be delivered to the SDR system located on the International Space Station as part of an experimental program. To support the proposed cognitive engine algorithm development, more realistic satellite-based communications channels are proposed along with rain attenuation synthesizers for LEO orbits, channel state detection algorithms, and multipath coefficients function of the reflector's electrical characteristics. The achieved performance of the proposed solutions are compared with the state-of-the-art, and novel performance benchmarks are provided for future research to reference.
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Ha, Youngmin. "Machine learning in quantitative finance." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8558/.
Full textAbstract:
This thesis consists of the three chapters. Chapter 1 aims to decrease the time complexity of multi-output relevance vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output dimensions, M is the number of basis functions, and V<M. The experimental results demonstrate that the proposed method is more competitive than the existing method, with regard to computation time. MATLAB codes are available at http://www.mathworks.com/matlabcentral/fileexchange/49131. The performance of online (sequential) portfolio selection (OPS), which rebalances a portfolio in every period (e.g. daily or weekly) in order to maximise the portfolio's expected terminal wealth in the long run, has been overestimated by the ideal assumption of unlimited market liquidity (i.e. no market impact costs). Therefore, a new transaction cost factor model that considers market impact costs, estimated from limit order book data, as well as proportional transaction costs (e.g. brokerage commissions or transaction taxes in a fixed percentage) is proposed in Chapter 2 for both measuring OPS performance in a more practical way and developing a new OPS method. Backtesting results from the historical limit order book data of NASDAQ-traded stocks show both the performance deterioration of OPS by the market impact costs and the superiority of the proposed OPS method in the environment of limited market liquidity. MATLAB codes are available at http://www.mathworks.com/matlabcentral/fileexchange/56496. Chapter 3 proposes an optimal intraday trading strategy to absorb the shock to the stock market when an online portfolio selection algorithm rebalances a portfolio. It considers real-time data of limit order books and splits a very large market order into a number of consecutive market orders to minimise overall transaction costs, consisting of market impact costs as well as proportional transaction costs. To be specific, it optimises both the number of intraday tradings and an intraday trading path for a multi-asset portfolio. Backtesting results from the historical limit order book data of NASDAQ-traded stocks show the superiority of the proposed trading algorithm in the environment of limited market liquidity. MATLAB codes are available at http://www.mathworks.com/matlabcentral/fileexchange/62503.
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Rouet-Leduc, Bertrand. "Machine learning for materials science." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/267987.
Full textAbstract:
Machine learning is a branch of artificial intelligence that uses data to automatically build inferences and models designed to generalise and make predictions. In this thesis, the use of machine learning in materials science is explored, for two different problems: the optimisation of gallium nitride optoelectronic devices, and the prediction of material failure in the setting of laboratory earthquakes. Light emitting diodes based on III-nitrides quantum wells have become ubiquitous as a light source, owing to their direct band-gap that covers UV, visible and infra-red light, and their very high quantum efficiency. This efficiency originates from most electronic transitions across the band-gap leading to the emission of a photon. At high currents however this efficiency sharply drops. In chapters 3 and 4 simulations are shown to provide an explanation for experimental results, shedding a new light on this drop of efficiency. Chapter 3 provides a simple and yet accurate model that explains the experimentally observed beneficial effect that silicon doping has on light emitting diodes. Chapter 4 provides a model for the experimentally observed detrimental effect that certain V-shaped defects have on light emitting diodes. These results pave the way for the association of simulations to detailed multi-microscopy. In the following chapters 5 to 7, it is shown that machine learning can leverage the use of device simulations, by replacing in a targeted and efficient way the very labour intensive tasks of making sure the numerical parameters of the simulations lead to convergence, and that the physical parameters reproduce experimental results. It is then shown that machine learning coupled with simulations can find optimal light emitting diodes structures, that have a greatly enhanced theoretical efficiency. These results demonstrate the power of machine learning for leveraging and automatising the exploration of device structures in simulations. Material failure is a very broad problem encountered in a variety of fields, ranging from engineering to Earth sciences. The phenomenon stems from complex and multi-scale physics, and failure experiments can provide a wealth of data that can be exploited by machine learning. In chapter 8 it is shown that by recording the acoustic waves emitted during the failure of a laboratory fault, an accurate predictive model can be built. The machine learning algorithm that is used retains the link with the physics of the experiment, and a new signal is thus discovered in the sound emitted by the fault. This new signal announces an upcoming laboratory earthquake, and is a signature of the stress state of the material. These results show that machine learning can help discover new signals in experiments where the amount of data is very large, and demonstrate a new method for the prediction of material failure.
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Shih, Lawrence Kai 1974. "Machine learning on Web documents." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28331.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (leaves 111-115).<br>The Web is a tremendous source of information: so tremendous that it becomes difficult for human beings to select meaningful information without support. We discuss tools that help people deal with web information, by, for example, blocking advertisements, recommending interesting news, and automatically sorting and compiling documents. We adapt and create machine learning algorithms for use with the Web's distinctive structures: large-scale, noisy, varied data with potentially rich, human-oriented features. We adapt two standard classification algorithms, the slow but powerful support vector machine and the fast but inaccurate Naive Bayes, to make them more effective for the Web. The support vector machine, which cannot currently handle the large amount of Web data potentially available, is sped up by "bundling" the classifier inputs to reduce the input size. The Naive Bayes classifier is improved through a series of three techniques aimed at fixing some of the severe, inaccurate assumptions Naive Bayes makes. Classification can also be improved by exploiting the Web's rich, human-oriented structure, including the visual layout of links on a page and the URL of a document. These "tree-shaped features" are placed in a Bayesian mutation model and learning is accomplished with a fast, online learning algorithm for the model. These new methods are applied to a personalized news recommendation tool, "the Daily You." The results of a 176 person user-study of news preferences indicate that the new Web-centric techniques out-perform classifiers that use traditional text algorithms and features. We also show that our methods produce an automated ad-blocker that performs as well as a hand-coded commercial ad-blocker.<br>by Lawrence Kai Shih.<br>Ph.D.
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Evgeniou, Theodoros K. (Theodoros Kostantinos) 1974. "Learning with kernel machine architectures." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86442.
Full textAbstract:
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 99-106).<br>by Theodoros K. Evgeniou.<br>Ph.D.
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Verleyen, Wim. "Machine learning for systems pathology." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4512.
Full textAbstract:
Systems pathology attempts to introduce more holistic approaches towards pathology and attempts to integrate clinicopathological information with “-omics” technology. This doctorate researches two examples of a systems approach for pathology: (1) a personalized patient output prediction for ovarian cancer and (2) an analytical approach differentiates between individual and collective tumour invasion. During the personalized patient output prediction for ovarian cancer study, clinicopathological measurements and proteomic biomarkers are analysed with a set of newly engineered bioinformatic tools. These tools are based upon feature selection, survival analysis with Cox proportional hazards regression, and a novel Monte Carlo approach. Clinical and pathological data proves to have highly significant information content, as expected; however, molecular data has little information content alone, and is only significant when selected most-informative variables are placed in the context of the patient's clinical and pathological measures. Furthermore, classifiers based on support vector machines (SVMs) that predict one-year PFS and three-year OS with high accuracy, show how the addition of carefully selected molecular measures to clinical and pathological knowledge can enable personalized prognosis predictions. Finally, the high-performance of these classifiers are validated on an additional data set. A second study, an analytical approach differentiates between individual and collective tumour invasion, analyses a set of morphological measures. These morphological measurements are collected with a newly developed process using automated imaging analysis for data collection in combination with a Bayesian network analysis to probabilistically connect morphological variables with tumour invasion modes. Between an individual and collective invasion mode, cell-cell contact is the most discriminating morphological feature. Smaller invading groups were typified by smoother cellular surfaces than those invading collectively in larger groups. Interestingly, elongation was evident in all invading cell groups and was not a specific feature of single cell invasion as a surrogate of epithelialmesenchymal transition. In conclusion, the combination of automated imaging analysis and Bayesian network analysis provides an insight into morphological variables associated with transition of cancer cells between invasion modes. We show that only two morphologically distinct modes of invasion exist. The two studies performed in this thesis illustrate the potential of a systems approach for pathology and illustrate the need of quantitative approaches in order to reveal the system behind pathology.
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Ainali, Chrysanthi. "Machine learning for translational medicine." Thesis, King's College London (University of London), 2013. https://kclpure.kcl.ac.uk/portal/en/theses/machine-learning-for-translational-medicine(c0dc2716-6211-4273-a5be-98eb1032f8da).html.
Full textAbstract:
In biomedical sciences, the increasing amount of available high through put data brings many challenges. The collection of such data usually results in large number of predictor genes and few samples, possibly also with high noise levels. Such problems are associated to the so called "curse of dimensionality", i.e. the small n large p problem. Therefore, the development and application of computational protocols in bioinformatics is necessary in order to tackle these problems, translate knowledge discovery from genome-scale studies and infer new knowledge combining the different types of post-genomics data. Data mining methods, including machine learning approaches, aim to identify patterns in high-throughput data and extract information about the underlying biological interactions. Research questions that are discussed in this thesis are disease stratification, biomarker discovery, network inference and data integration. The methodological contributions of this thesis focus on the problem encountered, nowadays, by clinicians where patients appearing to have the same disease may not respond to the same treatment. First, using supervised and unsupervised learning techniques, a machine learning strategy based on ensembles of decision trees was used to define sub phenotypes based on gene expression patterns and generate potential biomarkers for disease progression. Second, we developed a network inference algorithm (NetCFS) that uses feature selection to select a number of genes (n) that are highly correlated with the phenotype of interest so as to generate n different regression problems. Third, a "top-down" approach was implemented where gene sets corresponding to biochemical pathways are used to develop a disease classification framework. A multi-stage procedure was developed to uncover functional modules that are closely associated to the phenotype of interest and relevant to disease pathology. Phenotype-Responsive Genes (PRGs) are identified based on non-overlapping constraints of the classification procedure and association rules are used to estimate the activity level of each pathway. Applications discussed in this thesis include skin inflammation where an integrative approach combining clinically relevant in vivo models with molecular network analysis was developed to infer disease biomarkers and to translate the rapidly growing body of data into knowledge usable at the bedside. Other disease cases studied involve cancer analyses to illustrate contributions in systems medicine. Overall, this thesis presents methodological contributions on predictive models based on machine learning techniques and mathematical programming together with relevant insights in disease mechanisms and potential treatment options.