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Dissertations / Theses on the topic 'Theory, Machine learning'
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Hussain, Z. "Sparsity in machine learning : theory and practice." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/1444276/.
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The thesis explores sparse machine learning algorithms for supervised (classification and regression) and unsupervised (subspace methods) learning. For classification, we review the set covering machine (SCM) and propose new algorithms that directly minimise the SCMs sample compression generalisation error bounds during the training phase. Two of the resulting algorithms are proved to produce optimal or near-optimal solutions with respect to the loss bounds they minimise. One of the SCM loss bounds is shown to be incorrect and a corrected derivation of the sample compression bound is given along with a framework for allowing asymmetrical loss in sample compression risk bounds. In regression, we analyse the kernel matching pursuit (KMP) algorithm and derive a loss bound that takes into account the dual sparse basis vectors. We make connections to a sparse kernel principal components analysis (sparse KPCA) algorithm and bound its future loss using a sample compression argument. This investigation suggests a similar argument for kernel canonical correlation analysis (KCCA) and so the application of a similar sparsity algorithm gives rise to the sparse KCCA algorithm. We also propose a loss bound for sparse KCCA using the novel technique developed for KMP. All of the algorithms and bounds proposed in the thesis are elucidated with experiments.
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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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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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Carlucci, Lorenzo. "Some cognitively-motivated learning paradigms in Algorithmic Learning Theory." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 0.68 Mb., p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3220797.
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Li, Xiao. "Regularized adaptation : theory, algorithms, and applications /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5928.
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Blankenship, Jessica. "Machine Learning and Achievement Games." University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1590713726030926.
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Foreman, Samuel Alfred. "Learning better physics: a machine learning approach to lattice gauge theory." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6944.
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In this work we explore how lattice gauge theory stands to benefit from new developments in machine learning, and look at two specific examples that illustrate this point. We begin with a brief overview of selected topics in machine learning for those who may be unfamiliar, and provide a simple example that helps to show how these ideas are carried out in practice.
After providing the relevant background information, we then introduce an example of renormalization group (RG) transformations, inspired by the tensor RG, that can be used for arbitrary image sets, and look at applying this idea to equilibrium configurations of the two-dimensional Ising model.
The second main idea presented in this thesis involves using machine learning to improve the efficiency of Markov Chain Monte Carlo (MCMC) methods. Explicitly, we describe a new technique for performing Hamiltonian Monte Carlo (HMC) simulations using an alternative leapfrog integrator that is parameterized by weights in a neural network. This work is based on the L2HMC ('Learning to Hamiltonian Monte Carlo') algorithm introduced in [1].
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Sandberg, Martina. "Credit Risk Evaluation using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138968.
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In this thesis, we examine the machine learning models logistic regression, multilayer perceptron and random forests in the purpose of discriminate between good and bad credit applicants. In addition to these models we address the problem of imbalanced data with the Synthetic Minority Over-Sampling Technique (SMOTE). The data available have 273 286 entries and contains information about the invoice of the applicant and the credit decision process as well as information about the applicant. The data was collected during the period 2015-2017. With AUC-values at about 73%some patterns are found that can discriminate between customers that are likely to pay their invoice and customers that are not. However, the more advanced models only performed slightly better than the logistic regression.
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Shi, Bin. "A Mathematical Framework on Machine Learning: Theory and Application." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3876.
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The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application, we apply the optimization algorithms in sparse subspace clustering. A new algorithm, CoCoSSC, is proposed to improve the current sample complexity under the condition of the existence of noise and missing entries.
Gradient-based optimization methods have been increasingly modeled and inter- preted by ordinary differential equations (ODEs). Existing ODEs in the literature are, however, inadequate to distinguish between two fundamentally different meth- ods, Nesterov’s acceleration gradient method for strongly convex functions (NAG-SC) and Polyak’s heavy-ball method. In this paper, we derive high-resolution ODEs as more accurate surrogates for the two methods in addition to Nesterov’s acceleration gradient method for general convex functions (NAG-C), respectively. These novel ODEs can be integrated into a general framework that allows for a fine-grained anal- ysis of the discrete optimization algorithms through translating properties of the amenable ODEs into those of their discrete counterparts. As a first application of this framework, we identify the effect of a term referred to as gradient correction in NAG-SC but not in the heavy-ball method, shedding deep insight into why the for- mer achieves acceleration while the latter does not. Moreover, in this high-resolution ODE framework, NAG-C is shown to boost the squared gradient norm minimization at the inverse cubic rate, which is the sharpest known rate concerning NAG-C itself. Finally, by modifying the high-resolution ODE of NAG-C, we obtain a family of new optimization methods that are shown to maintain the accelerated convergence rates as NAG-C for minimizing convex functions.
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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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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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Wang, Sinong. "Coded Computation for Speeding up Distributed Machine Learning." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555336880521062.
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Ruan, Yongshao. "Efficient inference : a machine learning approach /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/7009.
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Narasimhan, Mukund. "Applications of submodular minimization in machine learning /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5983.
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Enver, Asad. "Modeling Trouble Ticket ResolutionTime Using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176779.
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This thesis work, conducted at Telenor Sweden, aims to build a model that would try to accurately predict the resolution time of Priority 4 Trouble Tickets. (Priority 4 trouble tickets are those tickets that get generated more often-e in higher volumes per month). It explores and investigates the possibility of applying Machine Learning and Deep Learning techniques to trouble ticket data to find an optimal solution that performs better than the current method in place (which is explained in Section 3.5). The model would be used by Telenor to inform the end-users of when the networks team expects to resolve the issues that are affecting them.
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Zhendong, Wang. "Error Pattern Recognition Using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150589.
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Mobile networks use automated continuous integration to secure the new technologies, which must reach high quality and backwards compatibility. The machinery needs to be constantly improved to meet the high demands that exist today and will evolve in the future. When testing products in large scale in a telecommunication environment, many parameters may be causing the error. Machine learning can help to assign troubleshooting labels and identify problematic areas in the test environment. In this thesis project, different modeling approaches will be applied step-wise. First, both the TF-IDF (term frequency-inverse document frequency) method and Topic model- ing will be applied for constructing variables. Since the TF-IDF method generates high dimensional variables in this case, Principal component analysis (PCA) is considered as a regularization method to reduce the dimensions. The results of this part will be evaluated by using different criteria. After the variable construction, two semi-supervised models called Label propagation and Label spreading will be applied for the purpose of assigning troubleshooting labels. In both algorithms, one weight matrix for measuring the similarities between different cases needs to be constructed. Two different methods for building up the weight matrix will be tested separately: Gaussian kernel and the nearest-neighbor method. Different hyperparameters in these two algorithms will be experimented with, to select the one which will return the optimal results. After the optimal model is selected, the unlabeled data will be divided up in different proportions for fitting the model. This is to test if the proportions of unlabeled data will affect the result of semi-supervised learning in our case. The classification results from the modeling part will be examined using three classical measures: accuracy, precision and recall. In addition, random permutations cross- validation is applied for the evaluation.
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Li, Zheng. "Accelerating Catalyst Discovery via Ab Initio Machine Learning." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/95915.
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In recent decades, machine learning techniques have received an explosion of interest in the domain of high-throughput materials discovery, which is largely attributed to the fastgrowing development of quantum-chemical methods and learning algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The framework, which integrates the descriptor-based kinetic analysis, material fingerprinting and machine learning algorithms, can rapidly explore a broad range of materials space with enormous compositional and configurational degrees of freedom prior to the expensive quantum-chemical calculations and/or experimental testing. Importantly, advanced machine learning approaches (e.g., global sensitivity analysis, principal component analysis, and exploratory analysis) can be utilized to shed light on the underlying physical factors governing the catalytic activity on a diverse type of catalytic materials with different applications. Chapter 1 introduces some basic concepts and knowledge relating to the computational catalyst design. Chapter 2 and Chapter 3 demonstrate the methodology to construct the machine-learning models for bimetallic catalysts. In Chapter 4, the multi-functionality of the machine-learning models is illustrated to understand the metalloporphyrin's underlying structure-property relationships. In Chapter 5, an uncertainty-guided machine learning strategy is introduced to tackle the challenge of data deficiency for perovskite electrode materials design in the electrochemical water splitting cell.<br>Doctor of Philosophy<br>Machine learning and deep learning techniques have revolutionized a range of industries in recent years and have huge potential to improve every aspect of our daily lives. Essentially, machine-learning provides algorithms the ability to automatically discover the hidden patterns of data without being explicitly programmed. Because of this, machine learning models have gained huge successes in applications such as website recommendation systems, online fraud detection, robotic technologies, image recognition, etc. Nevertheless, implementing machine-learning techniques in the field of catalyst design remains difficult due to 2 primary challenges. The first challenge is our insufficient knowledge about the structure-property relationships for diverse material systems. Typically, developing a physically intuitive material feature method requests in-depth expert knowledge about the underlying physics of the material system and it is always an active field. The second challenge is the lack of training data in academic research. In many cases, collecting a sufficient amount of training data is not always feasible due to the limitation of computational/experimental resources. Subsequently, the machine learning model optimized with small data tends to be over-fitted and could provide biased predictions with huge uncertainties. To address the above-mentioned challenges, this thesis focus on the development of robust feature methods and strategies for a variety of catalyst systems using the density functional theory (DFT) calculations. Through the case studies in the chapters, we show that the bulk electronic structure characteristics are successful features for capturing the adsorption properties of metal alloys and metal oxides. While molecular graphs are robust features for the molecular property, e.g., energy gap, of metal-organics compounds. Besides, we demonstrate that the adaptive machine learning workflow is an effective strategy to tackle the data deficiency issue in search of perovskite catalysts for the oxygen evolution reaction.
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Osama, Muhammad. "Machine learning for spatially varying data." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429234.
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Many physical quantities around us vary across space or space-time. An example of a spatial quantity is provided by the temperature across Sweden on a given day and as an example of a spatio-temporal quantity we observe the counts of the corona virus cases across the globe. Spatial and spatio-temporal data enable opportunities to answer many important questions. For example, what the weather would be like tomorrow or where the highest risk for occurrence of a disease is in the next few days? Answering questions such as these requires formulating and learning statistical models. One of the challenges with spatial and spatio-temporal data is that the size of data can be extremely large which makes learning a model computationally costly. There are several means of overcoming this problem by means of matrix manipulations and approximations. In paper I, we propose a solution to this problem where the model islearned in a streaming fashion, i.e., as the data arrives point by point. This also allows for efficient updating of the learned model based on newly arriving data which is very pertinent to spatio-temporal data. Another interesting problem in the spatial context is to study the causal effect that an exposure variable has on a response variable. For instance, policy makers might be interested in knowing whether increasing the number of police in a district has the desired effect of reducing crimes there. The challenge here is that of spatial confounding. A spatial map of the number of police against the spatial map of the number of crimes in different districts might show a clear association between these two quantities. However, there might be a third unobserved confounding variable that makes both quantities small and large together. In paper II, we propose a solution for estimating causal effects in the presence of such a confounding variable. Another common type of spatial data is point or event data, i.e., the occurrence of events across space. The event could for example be a reported disease or crime and one may be interested in predicting the counts of the event in a given region. A fundamental challenge here is to quantify the uncertainty in the predicted counts in a model in a robust manner. In paper III, we propose a regularized criterion for learning a predictive model of counts of events across spatial regions.The regularization ensures tighter prediction intervals around the predicted counts and have valid coverage irrespective of the degree of model misspecification.
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Verleyen, Wim. "Machine learning for systems pathology." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4512.
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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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Yu, Guoqiang. "Machine Learning to Interrogate High-throughput Genomic Data: Theory and Applications." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/28980.
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The missing heritability in genome-wide association studies (GWAS) is an intriguing open scientific problem which has attracted great recent interest. The interaction effects among risk factors, both genetic and environmental, are hypothesized to be one of the main missing heritability sources. Moreover, detection of multilocus interaction effect may also have great implications for revealing disease/biological mechanisms, for accurate risk prediction, personalized clinical management, and targeted drug design. However, current analysis of GWAS largely ignores interaction effects, partly due to the lack of tools that meet the statistical and computational challenges posed by taking into account interaction effects. Here, we propose a novel statistically-based framework (Significant Conditional Association) for systematically exploring, assessing significance, and detecting interaction effect. Further, our SCA work has also revealed new theoretical results and insights on interaction detection, as well as theoretical performance bounds. Using in silico data, we show that the new approach has detection power significantly better than that of peer methods, while controlling the running time within a permissible range. More importantly, we applied our methods on several real data sets, confirming well-validated interactions with more convincing evidence (generating smaller p-values and requiring fewer samples) than those obtained through conventional methods, eliminating inconsistent results in the original reports, and observing novel discoveries that are otherwise undetectable. The proposed methods provide a useful tool to mine new knowledge from existing GWAS and generate new hypotheses for further research.
Microarray gene expression studies provide new opportunities for the molecular characterization of heterogeneous diseases. Multiclass gene selection is an imperative task for identifying phenotype-associated mechanistic genes and achieving accurate diagnostic classification. Most existing multiclass gene selection methods heavily rely on the direct extension of two-class gene selection methods. However, simple extensions of binary discriminant analysis to multiclass gene selection are suboptimal and not well-matched to the unique characteristics of the multi-category classification problem. We report a simpler and yet more accurate strategy than previous works for multicategory classification of heterogeneous diseases. Our method selects the union of one-versus-everyone phenotypic up-regulated genes (OVEPUGs) and matches this gene selection with a one-versus-rest support vector machine. Our approach provides even-handed gene resources for discriminating both neighboring and well-separated classes, and intends to assure the statistical reproducibility and biological plausibility of the selected genes. We evaluated the fold changes of OVEPUGs and found that only a small number of high-ranked genes were required to achieve superior accuracy for multicategory classification. We tested the proposed OVEPUG method on six real microarray gene expression data sets (five public benchmarks and one in-house data set) and two simulation data sets, observing significantly improved performance with lower error rates, fewer marker genes, and higher performance sustainability, as compared to several widely-adopted gene selection and classification methods.<br>Ph. D.
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Zhang, Yue. "Sparsity in Image Processing and Machine Learning: Modeling, Computation and Theory." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1523017795312546.
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Altun, Gulsah. "Machine Learning and Graph Theory Approaches for Classification and Prediction of Protein Structure." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/cs_diss/31.
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Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widely studied; however it has only been recently applied to bioinformatics. In this work, we introduced new algorithms based on statistical methods, graph theory concepts and machine learning for the protein structure prediction problem. A new statistical method based on z-scores has been introduced for seed selection in proteins. A new method based on finding common cliques in protein data for feature selection is also introduced, which reduces noise in the data. We also introduced new binary classifiers for the prediction of structural transitions in proteins. These new binary classifiers achieve much higher accuracy results than the current traditional binary classifiers.
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Huszár, Ferenc. "Scoring rules, divergences and information in Bayesian machine learning." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648333.
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Cheng, Jie. "Learning Bayesian networks from data : an information theory based approach." Thesis, University of Ulster, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243621.
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Ammar, Kareem. "Multi-heuristic theory assessment with iterative selection." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3701.
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Thesis (M.S.)--West Virginia University, 2004.<br>Title from document title page. Document formatted into pages; contains viii, 106 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 105-106).
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Westerlund, Fredrik. "CREDIT CARD FRAUD DETECTION (Machine learning algorithms)." Thesis, Umeå universitet, Statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136031.
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Credit card fraud is a field with perpetrators performing illegal actions that may affect other individuals or companies negatively. For instance, a criminalcan steal credit card information from an account holder and then conduct fraudulent transactions. The activities are a potential contributory factor to how illegal organizations such as terrorists and drug traffickers support themselves financially. Within the machine learning area, there are several methods that possess the ability to detect credit card fraud transactions; supervised learning and unsupervised learning algorithms. This essay investigates the supervised approach, where two algorithms (Hellinger Distance Decision Tree (HDDT) and Random Forest) are evaluated on a real life dataset of 284,807 transactions. Under those circumstances, the main purpose is to develop a “well-functioning” model with a reasonable capacity to categorize transactions as fraudulent or legit. As the data is heavily unbalanced, reducing the false-positive rate is also an important part when conducting research in the chosen area. In conclusion, evaluated algorithms present a fairly similar outcome, where both models have the capability to distinguish the classes from each other. However, the Random Forest approach has a better performance than HDDT in all measures of interest.
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Nichols, Timothy A. "Explaining dual-task implicit learning deficits the effect of withing stimulus presentation /." Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-04022006-232232/.
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Thesis (Ph. D.)--Psychology, Georgia Institute of Technology, 2006.<br>Daniel Spieler, Committee Member ; Dennis Folds, Committee Member ; Arthur Fisk, Committee Chair ; Wendy Rogers, Committee Member ; Eric Schumacher, Committee Member.
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Ewö, Christian. "A machine learning approach in financial markets." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5571.
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In this work we compare the prediction performance of three optimized technical indicators with a Support Vector Machine Neural Network. For the indicator part we picked the common used indicators: Relative Strength Index, Moving Average Convergence Divergence and Stochastic Oscillator. For the Support Vector Machine we used a radial-basis kernel function and regression mode. The techniques were applied on financial time series brought from the Swedish stock market. The comparison and the promising results should be of interest for both finance people using the techniques in practice, as well as software companies and similar considering to implement the techniques in their products.
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Korba, Anna. "Learning from ranking data : theory and methods." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT009/document.
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Les données de classement, c.à. d. des listes ordonnées d'objets, apparaissent naturellement dans une grande variété de situations, notamment lorsque les données proviennent d’activités humaines (bulletins de vote d'élections, enquêtes d'opinion, résultats de compétitions) ou dans des applications modernes du traitement de données (moteurs de recherche, systèmes de recommendation). La conception d'algorithmes d'apprentissage automatique, adaptés à ces données, est donc cruciale. Cependant, en raison de l’absence de structure vectorielle de l’espace des classements et de sa cardinalité explosive lorsque le nombre d'objets augmente, la plupart des méthodes classiques issues des statistiques et de l’analyse multivariée ne peuvent être appliquées directement. Par conséquent, la grande majorité de la littérature repose sur des modèles paramétriques. Dans cette thèse, nous proposons une théorie et des méthodes non paramétriques pour traiter les données de classement. Notre analyse repose fortement sur deux astuces principales. La première est l’utilisation poussée de la distance du tau de Kendall, qui décompose les classements en comparaisons par paires. Cela nous permet d'analyser les distributions sur les classements à travers leurs marginales par paires et à travers une hypothèse spécifique appelée transitivité, qui empêche les cycles dans les préférences de se produire. La seconde est l'utilisation des fonctions de représentation adaptées aux données de classements, envoyant ces dernières dans un espace vectoriel. Trois problèmes différents, non supervisés et supervisés, ont été abordés dans ce contexte: l'agrégation de classement, la réduction de dimensionnalité et la prévision de classements avec variables explicatives.La première partie de cette thèse se concentre sur le problème de l'agrégation de classements, dont l'objectif est de résumer un ensemble de données de classement par un classement consensus. Parmi les méthodes existantes pour ce problème, la méthode d'agrégation de Kemeny se démarque. Ses solutions vérifient de nombreuses propriétés souhaitables, mais peuvent être NP-difficiles à calculer. Dans cette thèse, nous avons étudié la complexité de ce problème de deux manières. Premièrement, nous avons proposé une méthode pour borner la distance du tau de Kendall entre tout candidat pour le consensus (généralement le résultat d'une procédure efficace) et un consensus de Kemeny, sur tout ensemble de données. Nous avons ensuite inscrit le problème d'agrégation de classements dans un cadre statistique rigoureux en le reformulant en termes de distributions sur les classements, et en évaluant la capacité de généralisation de consensus de Kemeny empiriques.La deuxième partie de cette théorie est consacrée à des problèmes d'apprentissage automatique, qui se révèlent être étroitement liés à l'agrégation de classement. Le premier est la réduction de la dimensionnalité pour les données de classement, pour lequel nous proposons une approche de transport optimal, pour approximer une distribution sur les classements par une distribution montrant un certain type de parcimonie. Le second est le problème de la prévision des classements avec variables explicatives, pour lesquelles nous avons étudié plusieurs méthodes. Notre première proposition est d’adapter des méthodes constantes par morceaux à ce problème, qui partitionnent l'espace des variables explicatives en régions et assignent à chaque région un label (un consensus). Notre deuxième proposition est une approche de prédiction structurée, reposant sur des fonctions de représentations, aux avantages théoriques et computationnels, pour les données de classements<br>Ranking data, i.e., ordered list of items, naturally appears in a wide variety of situations, especially when the data comes from human activities (ballots in political elections, survey answers, competition results) or in modern applications of data processing (search engines, recommendation systems). The design of machine-learning algorithms, tailored for these data, is thus crucial. However, due to the absence of any vectorial structure of the space of rankings, and its explosive cardinality when the number of items increases, most of the classical methods from statistics and multivariate analysis cannot be applied in a direct manner. Hence, a vast majority of the literature rely on parametric models. In this thesis, we propose a non-parametric theory and methods for ranking data. Our analysis heavily relies on two main tricks. The first one is the extensive use of the Kendall’s tau distance, which decomposes rankings into pairwise comparisons. This enables us to analyze distributions over rankings through their pairwise marginals and through a specific assumption called transitivity, which prevents cycles in the preferences from happening. The second one is the extensive use of embeddings tailored to ranking data, mapping rankings to a vector space. Three different problems, unsupervised and supervised, have been addressed in this context: ranking aggregation, dimensionality reduction and predicting rankings with features.The first part of this thesis focuses on the ranking aggregation problem, where the goal is to summarize a dataset of rankings by a consensus ranking. Among the many ways to state this problem stands out the Kemeny aggregation method, whose solutions have been shown to satisfy many desirable properties, but can be NP-hard to compute. In this work, we have investigated the hardness of this problem in two ways. Firstly, we proposed a method to upper bound the Kendall’s tau distance between any consensus candidate (typically the output of a tractable procedure) and a Kemeny consensus, on any dataset. Then, we have casted the ranking aggregation problem in a rigorous statistical framework, reformulating it in terms of ranking distributions, and assessed the generalization ability of empirical Kemeny consensus.The second part of this thesis is dedicated to machine learning problems which are shown to be closely related to ranking aggregation. The first one is dimensionality reduction for ranking data, for which we propose a mass-transportation approach to approximate any distribution on rankings by a distribution exhibiting a specific type of sparsity. The second one is the problem of predicting rankings with features, for which we investigated several methods. Our first proposal is to adapt piecewise constant methods to this problem, partitioning the feature space into regions and locally assigning as final label (a consensus ranking) to each region. Our second proposal is a structured prediction approach, relying on embedding maps for ranking data enjoying theoretical and computational advantages
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Wu, Bolin. "PREDICTION OF DRUG INDICATION LIST BY MACHINE LEARNING." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447232.
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The motivation of this thesis originates from the cooperation with Uppsala Monitoring Centre, a WHO collaborating centre for international drug monitoring. The research question is how to give a good summary of the drug indication list. This thesis proposes a regression tree, Random Forests and XGBoost, known as tree-based models to predict the drug indication summary based on its user statistics and pharmaceutical information. Besides, this thesis also compares the aforementioned tree-based models' prediction performance with the baseline models, which are basic linear regression and support vector regression SVR. The analysis shows SVR with RBF kernel and post-pruning tree are the best models to answer the research question.
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Hedblom, Edvin, and Rasmus Åkerblom. "Debt recovery prediction in securitized non-performing loans using machine learning." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252311.
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Credit scoring using machine learning has been gaining attention within the research field in recent decades and it is widely used in the financial sector today. Studies covering binary credit scoring of securitized non-performing loans are however very scarce. This paper is using random forest and artificial neural networks to predict debt recovery for such portfolios. As a performance benchmark, logistic regression is used. Due to the nature of high imbalance between the classes, the performance is evaluated mainly on the area under both the receiver operating characteristic curve and the precision-recall curve. This paper shows that random forest, artificial neural networks and logistic regression have similar performance. They all indicate an overall satisfactory ability to predict debt recovery and hold potential to be implemented in day-to-day business related to non-performing loans.<br>Bedömning av kreditvärdighet med maskininlärning har fått ökad uppmärksamhet inom forskningen under de senaste årtiondena och är ofta använt inom den finansiella sektorn. Tidigare studier inom binär klassificering av kreditvärdighet för icke-presterande lånportföljer är få. Denna studie använder random forest och artificial neural networks för att prediktera återupptagandet av lånbetalningar för sådana portföljer. Som jämförelse används logistisk regression. På grund av kraftig obalans mellan klasserna kommer modellerna att bedömas huvudsakligen på arean under reciever operating characteristic-kurvan och precision-recall-kurvan. Denna studie visar på att random forest, artificial neural networks och logistisk regression presterar likartat med överlag goda resultat som har potential att fördelaktigt implementeras i praktiken.
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Hild, Andreas. "ESTIMATING AND EVALUATING THE PROBABILITY OF DEFAULT – A MACHINE LEARNING APPROACH." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447385.
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In this thesis, we analyse and evaluate classification models for panel credit risk data. Variables are selected based on results from recursive feature elimination as well as economic reasoning where the probability of default is estimated. We employ several machine learning and statistical techniques and assess the performance of each model based on AUC, Brier score as well as the absolute mean difference between the predicted and the actual outcome, carried out with cross validation of four folds and extensive hyperparameter optimization. The LightGBM model had the best performance and many machine learning models showed a superior performance compared to traditional models like logistic regression. Hence, the results of this thesis show that machine learning models like gradient boosting models, neural networks and voting models have the capacity to challenge traditional statistical methods such as logistic regression within credit risk modelling.
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Mirzaikamrani, Sonya. "Predictive modeling and classification for Stroke using the machine learning methods." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-81837.
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Lindmaa, Alexander. "Theoretical prediction of properties of atomistic systems : Density functional theory and machine learning." Doctoral thesis, Linköpings universitet, Teoretisk Fysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139767.
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The prediction of ground state properties of atomistic systems is of vital importance in technological advances as well as in the physical sciences. Fundamentally, these predictions are based on a quantum-mechanical description of many-electron systems. One of the hitherto most prominent theories for the treatment of such systems is density functional theory (DFT). The main reason for its success is due to its balance of acceptable accuracy with computational efficiency. By now, DFT is applied routinely to compute the properties of atomic, molecular, and solid state systems. The general approach to solve the DFT equations is to use a density-functional approximation (DFA). In Kohn-Sham (KS) DFT, DFAs are applied to the unknown exchangecorrelation (xc) energy. In orbital-free DFT on the other hand, where the total energy is minimized directly with respect to the electron density, a DFA applied to the noninteracting kinetic energy is also required. Unfortunately, central DFAs in DFT fail to qualitatively capture many important aspects of electronic systems. Two prime examples are the description of localized electrons, and the description of systems where electronic edges are present. In this thesis, I use a model system approach to construct a DFA for the electron localization function (ELF). The very same approach is also taken to study the non-interacting kinetic energy density (KED) in the slowly varying limit of inhomogeneous electron densities, where the effect of electronic edges are effectively included. Apart from the work on model systems, extensions of an exchange energy functional with an improved KS orbital description are presented: a scheme for improving its description of energetics of solids, and a comparison of its description of an essential exact exchange feature known as the derivative discontinuity with numerical data for exact exchange. An emerging alternative route towards the prediction of the properties of atomistic systems is machine learning (ML). I present a number of ML methods for the prediction of solid formation energies, with an accuracy that is on par with KS DFT calculations, and with orders-of-magnitude lower computational cost.<br>Att kunna förutsäga egenskaper hos atomistiska system utgör en viktigdel av vår teknologiska utveckling, samt spelar en betydande roll i defysikaliska vetenskaperna. Sådana förutsägelser bygger på en kvantmekaniskbeskrivning av mångelektronsystem. En av de mest framståendeteorierna för att behandla den här typen av system är täthetsfunktionalteorin(DFT). Den främsta orsaken till dess framgång är attden lyckas kombinera skaplig noggrannhet med en bra beräkningseffektivitet.DFT används numera rutinmässigt för att beräkna storheterhos atomer, molekyler, och fasta kroppar. Generellt sett löses ekvationerna inom DFT genom att man inför entäthetsfunktionalapproximation (DFA). I Kohn-Sham (KS) DFT, användsDFAer för att approximera utbytes-korrelationsenergin. Inom orbitalfriDFT, där målet är att direkt minimera den totala energin med avseendepå elektrontätheten, så approximerar man också den icke-interageranderörelseenergin hos elektronerna. Dessvärre så fallerar många centralaDFAer att kvalitativt beskriva många viktiga aspekter hos elektronsystem.Två viktiga exempel är beskrivningen av lokaliserade elektroner,samt beskrivningen av system där det förekommer elektronytor. I denna avhandling använder jag modellsystem för att konstruera enDFAför elektronlokaliseringsfunktionen (ELF). Samma tillvägagångssättappliceras sedan för att studera den kinetiska energitätheten i gränsen avlångsamt varierande elektrontätheter, där effekten av elektronytor effektivtinkluderas. Förutom arbetet som berör modellsystem, så presenterasen utökad variant av en utbytes-energifunktional med en förbättrad KSorbitalbeskrivning: ett schema för att förbättra dess energiegenskaperför solida material, samt en jämförelse av dess beskrivning av en viktigegenskap hos den exakta utbytesenergin, vilket utgörs av diskontinuiteteri dess derivata. Ett mera nyligen uppkommet samt alternativt sätt att kunna förutsägaegenskaper hos atomistiska system utgörs av maskinlärning (ML).Jag presenterar ett antal ML-modeller för att kunna förutsäga formeringsenergierhos fasta material med en noggrannhet som är i linje medresultat som uppnås av beräkningar med hjälp av KS DFT, och med enberäkningseffektivitet som är flera storleksordningar snabbare.
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Yang, Ying. "Discretization for Naive-Bayes learning." Monash University, School of Computer Science and Software Engineering, 2003. http://arrow.monash.edu.au/hdl/1959.1/9393.
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Roberts, T. Dale (Terrance Dale) Carleton University Dissertation Computer Science. "Learning automata solutions to the capacity assignment problem." Ottawa, 1997.
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Lu, Yibiao. "Statistical methods with application to machine learning and artificial intelligence." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44730.
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This thesis consists of four chapters. Chapter 1 focuses on theoretical results on high-order laplacian-based regularization in function estimation. We studied the iterated laplacian regularization in the context of supervised learning in order to achieve both nice theoretical properties (like thin-plate splines) and good performance over complex region (like soap film smoother). In Chapter 2, we propose an innovative static path-planning algorithm called m-A* within an environment full of obstacles. Theoretically we show that m-A* reduces the number of vertex. In the simulation study, our approach outperforms A* armed with standard L1 heuristic and stronger ones such as True-Distance heuristics (TDH), yielding faster query time, adequate usage of memory and reasonable preprocessing time. Chapter 3 proposes m-LPA* algorithm which extends the m-A* algorithm in the context of dynamic path-planning and achieves better performance compared to the benchmark: lifelong planning A* (LPA*) in terms of robustness and worst-case computational complexity. Employing the same beamlet graphical structure as m-A*, m-LPA* encodes the information of the environment in a hierarchical, multiscale fashion, and therefore it produces a more robust dynamic path-planning algorithm. Chapter 4 focuses on an approach for the prediction of spot electricity spikes via a combination of boosting and wavelet analysis. Extensive numerical experiments show that our approach improved the prediction accuracy compared to those results of support vector machine, thanks to the fact that the gradient boosting trees method inherits the good properties of decision trees such as robustness to the irrelevant covariates, fast computational capability and good interpretation.
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Hamid, Muhammad Raffay. "Unsupervised Activity Discovery and Characterization for Sensor-Rich Environments." Thesis, Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/14131.
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This thesis presents an unsupervised method for discovering and analyzing the different
kinds of activities in an active environment. Drawing from natural language processing, a
novel representation of activities as bags of event n-grams is introduced, where the global
structural information of activities using their local event statistics is analyzed. It is demonstrated how maximal cliques in an undirected edge-weighted graph of activities, can be used in an unsupervised manner, to discover the different activity-classes. Taking on some work done in computer networks and bio-informatics, it is shown how to characterize these discovered activity-classes from a wholestic as well as a by-parts view-point. A definition of anomalous activities is formulated along with a way to detect them based on the difference of an activity instance from each of the discovered activity-classes. Finally, an information theoretic method to explain the detected anomalies in a human-interpretable form is presented. Results over extensive data-sets, collected from multiple active environments are
presented, to show the competence and generalizability of the proposed framework.
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Saket, Rishi. "Intractability results for problems in computational learning and approximation." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29681.
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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2009.<br>Committee Chair: Khot, Subhash; Committee Member: Tetali, Prasad; Committee Member: Thomas, Robin; Committee Member: Vempala, Santosh; Committee Member: Vigoda, Eric. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Ferdowsi, Khosrowshahi Aidin. "Distributed Machine Learning for Autonomous and Secure Cyber-physical Systems." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99466.
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Autonomous cyber-physical systems (CPSs) such as autonomous connected vehicles (ACVs), unmanned aerial vehicles (UAVs), critical infrastructure (CI), and the Internet of Things (IoT) will be essential to the functioning of our modern economies and societies. Therefore, maintaining the autonomy of CPSs as well as their stability, robustness, and security (SRS) in face of exogenous and disruptive events is a critical challenge. In particular, it is crucial for CPSs to be able to not only operate optimally in the vicinity of a normal state but to also be robust and secure so as to withstand potential failures, malfunctions, and intentional attacks. However, to evaluate and improve the SRS of CPSs one must overcome many technical challenges such as the unpredictable behavior of a CPS's cyber-physical environment, the vulnerability to various disruptive events, and the interdependency between CPSs. The primary goal of this dissertation is, thus, to develop novel foundational analytical tools, that weave together notions from machine learning, game theory, and control theory, in order to study, analyze, and optimize SRS of autonomous CPSs. Towards achieving this overarching goal, this dissertation led to several major contributions. First, a comprehensive control and learning framework was proposed to thwart cyber and physical attacks on ACV networks. This framework brings together new ideas from optimal control and reinforcement learning (RL) to derive a new optimal safe controller for ACVs in order to maximize the street traffic flow while minimizing the risk of accidents. Simulation results show that the proposed optimal safe controller outperforms the current state of the art controllers by maximizing the robustness of ACVs to physical attacks. Furthermore, using techniques from convex optimization and deep RL a joint trajectory and scheduling policy is proposed in UAV-assisted networks that aims at maintaining the freshness of ground node data at the UAV. The analytical and simulation results show that the proposed policy can outperform policies such discretized state RL and value-based methods in terms of maximizing the freshness of data.
Second, in the IoT domain, a novel watermarking algorithm, based on long short term memory cells, is proposed for dynamic authentication of IoT signals. The proposed watermarking algorithm is coupled with a game-theoretic framework so as to enable efficient authentication in massive IoT systems. Simulation results show that using our approach, IoT messages can be transmitted from IoT devices with an almost 100% reliability.
Next, a brainstorming generative adversarial network (BGAN) framework is proposed. It is shown that this framework can learn to generate real-looking data in a distributed fashion while preserving the privacy of agents (e.g. IoT devices, ACVs, etc). The analytical and simulation results show that the proposed BGAN architecture allows heterogeneous neural network designs for agents, works without reliance on a central controller, and has a lower communication over head compared to other state-of-the-art distributed architectures.
Last, but not least, the SRS challenges of interdependent CI (ICI) are addressed. Novel game-theoretic frameworks are proposed that allow the ICI administrator to assign different protection levels on ICI components to maximizing the expected ICI security. The mixed-strategy Nash of the games are derived analytically. Simulation results coupled with theoretical analysis show that, using the proposed games, the administrator can maximize the security level in ICI components. In summary, this dissertation provided major contributions across the areas of CPSs, machine learning, game theory, and control theory with the goal of ensuring SRS across various domains such as autonomous vehicle networks, IoT systems, and ICIs. The proposed approaches provide the necessary fundamentals that can lay the foundations of SRS in CPSs and pave the way toward the practical deployment of autonomous CPSs and applications.<br>Doctor of Philosophy<br>In order to deliver innovative technological services to their residents, smart cities will rely on autonomous cyber-physical systems (CPSs) such as cars, drones, sensors, power grids, and other networks of digital devices. Maintaining stability, robustness, and security (SRS) of those smart city CPSs is essential for the functioning of our modern economies and societies. SRS can be defined as the ability of a CPS, such as an autonomous vehicular system, to operate without disruption in its quality of service. In order to guarantee SRS of CPSs one must overcome many technical challenges such as CPSs' vulnerability to various disruptive events such as natural disasters or cyber attacks, limited resources, scale, and interdependency. Such challenges must be considered for CPSs in order to design vehicles that are controlled autonomously and whose motion is robust against unpredictable events in their trajectory, to implement stable Internet of digital devices that work with a minimum communication delay, or to secure critical infrastructure to provide services such as electricity, gas, and water systems. The primary goal of this dissertation is, thus, to develop novel foundational analytical tools, that weave together notions from machine learning, game theory, and control theory, in order to study, analyze, and optimize SRS of autonomous CPSs which eventually will improve the quality of service provided by smart cities. To this end, various frameworks and effective algorithms are proposed in order to enhance the SRS of CPSs and pave the way toward the practical deployment of autonomous CPSs and applications. The results show that the developed solutions can enable a CPS to operate efficiently while maintaining its SRS. As such, the outcomes of this research can be used as a building block for the large deployment of smart city technologies that can be of immense benefit to tomorrow's societies.
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Yu, Shen. "A Bayesian machine learning system for recognizing group behaviour." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:8881/R/?func=dbin-jump-full&object_id=32565.
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Srivastava, Santosh. "Bayesian minimum expected risk estimation of distributions for statistical learning /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6765.
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Ahlberg, Helgee Ernst. "Improving drug discovery decision making using machine learning and graph theory in QSAR modeling." Göteborg : Dept. of Chemistry, University of Gothenburg, 2010. http://gupea.ub.gu.se/dspace/handle/2077/21838.
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Arale, Brännvall Marian. "Accelerating longitudinal spinfluctuation theory for iron at high temperature using a machine learning method." Thesis, Linköpings universitet, Teoretisk Fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170314.
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In the development of materials, the understanding of their properties is crucial. For magnetic materials, magnetism is an apparent property that needs to be accounted for. There are multiple factors explaining the phenomenon of magnetism, one being the effect of vibrations of the atoms on longitudinal spin fluctuations. This effect can be investigated by simulations, using density functional theory, and calculating energy landscapes. Through such simulations, the energy landscapes have been found to depend on the magnetic background and the positions of the atoms. However, when simulating a supercell of many atoms, to calculate energy landscapes for all atoms consumes many hours on the supercomputer. In this thesis, the possibility of using machine learning models to accelerate the approximation of energy landscapes is investigated. The material under investigation is body-centered cubic iron in the paramagnetic state at 1043 K. Machine learning enables statistical predictions to be made on new data based on patterns found in a previous set of data. Kernel ridge regression is used as the machine learning method. An important issue when training a machine learning model is the representation of the data in the so called descriptor (feature vector representation) or, more specific to this case, how the environment of an atom in a supercell is accounted for and represented properly. Four different descriptors are developed and compared to investigate which one yields the best result and why. Apart from comparing the descriptors, the results when using machine learning models are compared to when using other methods to approximate the energy landscapes. The machine learning models are also tested in a combined atomistic spin dynamics and ab initio molecular dynamics simulation (ASD-AIMD) where they were used to approximate energy landscapes and, from that, magnetic moment magnitudes at 1043 K. The results of these simulations are compared to the results from two other cases: one where the magnetic moment magnitudes are set to a constant value and one where they are set to their magnitudes at 0 K. From these investigations it is found that using machine learning methods to approximate the energy landscapes does, to a large degree, decrease the errors compared to the other approximation methods investigated. Some weaknesses of the respective descriptors were detected and if, in future work, these are accounted for, the errors have the potential of being lowered further.
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Aleti, Kalyan Reddy. "E-quality control a support vector machines approach /." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Nyshadham, Chandramouli. "Materials Prediction Using High-Throughput and Machine Learning Techniques." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7735.
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Predicting new materials through virtually screening a large number of hypothetical materials using supercomputers has enabled materials discovery at an accelerated pace. However, the innumerable number of possible hypothetical materials necessitates the development of faster computational methods for speedier screening of materials reducing the time of discovery. In this thesis, I aim to understand and apply two computational methods for materials prediction. The first method deals with a computational high-throughput study of superalloys. Superalloys are materials which exhibit high-temperature strength. A combinatorial high-throughput search across 2224 ternary alloy systems revealed 102 potential superalloys of which 37 are brand new, all of which we patented. The second computational method deals with a machine-learning (ML) approach and aims at understanding the consistency among five different state-of-the-art machine-learning models in predicting the formation enthalpy of 10 different binary alloys. The study revealed that although the five different ML models approach the problem uniquely, their predictions are consistent with each other and that they are all capable of predicting multiple materials simultaneously.My contribution to both the projects included conceiving the idea, performing calculations, interpreting the results, and writing significant portions of the two journal articles published related to each project. A follow-up work of both computational approaches, their impact, and future outlook of materials prediction are also presented.
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Nichols, Timothy A. "Explaining dual-task implicit learning deficits: the effect of within stimulus presentation." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10490.
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Under typical between stimulus dual-task conditions, implicit sequence learning typically suffers, except under within stimulus conditions, where the stimuli for both tasks are the same. This finding is inconclusive, given that it has not been replicated and the study under which it was obtained was methodologically flawed. The finding also seemed to contradict the psychological refractory period finding that simultaneous presentation of the two task stimuli will result in performance decrements. Two experiments were conducted to test the effect of within stimulus presentation in a dual-task implicit learning task. In Experiment 1, within stimulus presentation resulted in improved sequence learning, relative to between stimulus presentation. The second experiment did not show an effect of response selection load under within stimulus presentation conditions. The findings suggest that implicit learning can occur under attentionally demanding conditions, but that the incidental task structure to be learned should be comprised of stimuli that are already attended during primary task processing.
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Feng, Zijie. "Machine learning methods for seasonal allergic rhinitis studies." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173090.
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Seasonal allergic rhinitis (SAR) is a disease caused by allergens from both environmental and genetic factors. Some researchers have studied the SAR based on traditional genetic methodologies. As technology develops, a new technique called single-cell RNA sequencing (scRNA-seq) is developed, which can generate high-dimension data. We apply two machine learning (ML) algorithms, random forest (RF) and partial least squares discriminant analysis (PLS-DA), for cell source classification and gene selection based on the SAR scRNA-seq time-series data from three allergic patients and four healthy controls denoised by single-cell variational inference (scVI). We additionally propose a new fitting method consisting of bootstrap and cubic smoothing splines to fit the averaged gene expressions per cell from different populations. To sum up, we find that both RF and PLS-DA could provide high classification accuracy, and RF is more preferable, considering its stable performance and strong gene-selection ability. Based on our analysis, there are 10 genes having discriminatory power to classify cells of allergic patients and healthy controls at any timepoints. Although there is no literature founded to show the direct connections between such 10 genes and SAR, the potential associations are indirectly confirmed by some studies. It shows a possibility that we can alarm allergic patients before a disease outbreak based on their genetic information. Meanwhile, our experiment results indicate that ML algorithms may discover something between genes and SAR compared with traditional techniques, which needs to be analyzed in genetics in the future.
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Porr, Bernd. "Sequence-learning in a self-referential closed-loop behavioural system." Thesis, University of Stirling, 2003. http://hdl.handle.net/1893/2582.
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This thesis focuses on the problem of "autonomous agents". It is assumed that such agents want to be in a desired state which can be assessed by the agent itself when it observes the consequences of its own actions. Therefore the feedback from the motor output via the environment to the sensor input is an essential component of such a system. As a consequence an agent is defined in this thesis as a self-referential system which operates within a closed sensor- mot or-sensor feedback loop. The generic situation is that the agent is always prone to unpredictable disturbances which arrive from the outside, i.e. from its environment. These disturbances cause a deviation from the desired state (for example the organism is attacked unexpectedly or the temperature in the environment changes, ...). The simplest mechanism for managing such disturbances in an organism is to employ a reflex loop which essentially establishes reactive behaviour. Reflex loops are directly related to closed loop feedback controllers. Thus, they are robust and they do not need a built-in model of the control situation. However, reflexes have one main disadvantage, namely that they always occur 'too late'; i.e., only after a (for example, unpleasant) reflex eliciting sensor event has occurred. This defines an objective problem for the organism. This thesis provides a solution to this problem which is called Isotropic Sequence Order (ISO-) learning. The problem is solved by correlating the primary reflex and a predictive sensor input: the result is that the system learns the temporal relation between the primary reflex and the earlier sensor input and creates a new predictive reflex. This (new) predictive reflex does not have the disadvantage of the primary reflex, namely of always being too late. As a consequence the agent is able to maintain its desired input-state all the time. In terms of engineering this means that ISO learning solves the inverse controller problem for the reflex, which is mathematically proven in this thesis. Summarising, this means that the organism starts as a reactive system and learning turns the system into a pro-active system. It will be demonstrated by a real robot experiment that ISO learning can successfully learn to solve the classical obstacle avoidance task without external intervention (like rewards). In this experiment the robot has to correlate a reflex (retraction after collision) with signals of range finders (turn before the collision). After successful learning the robot generates a turning reaction before it bumps into an obstacle. Additionally it will be shown that the learning goal of 'reflex avoidance' can also, paradoxically, be used to solve an attraction task.
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Huang, Xin. "A study on the application of machine learning algorithms in stochastic optimal control." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252541.
Full textAbstract:
By observing a similarity between the goal of stochastic optimal control to minimize an expected cost functional and the aim of machine learning to minimize an expected loss function, a method of applying machine learning algorithm to approximate the optimal control function is established and implemented via neural approximation. Based on a discretization framework, a recursive formula for the gradient of the approximated cost functional on the parameters of neural network is derived. For a well-known Linear-Quadratic-Gaussian control problem, the approximated neural network function obtained with stochastic gradient descent algorithm manages to reproduce to shape of the theoretical optimal control function, and application of different types of machine learning optimization algorithm gives quite close accuracy rate in terms of their associated empirical value function. Furthermore, it is shown that the accuracy and stability of machine learning approximation can be improved by increasing the size of minibatch and applying a finer discretization scheme. These results suggest the effectiveness and appropriateness of applying machine learning algorithm for stochastic optimal control.<br>Genom att observera en likhet mellan målet för stokastisk optimal styrning för att minimera en förväntad kostnadsfunktionell och syftet med maskininlärning att minimera en förväntad förlustfunktion etableras och implementeras en metod för att applicera maskininlärningsalgoritmen för att approximera den optimala kontrollfunktionen via neuralt approximation. Baserat på en diskretiseringsram, härleds en rekursiv formel för gradienten av den approximerade kostnadsfunktionen på parametrarna för neuralt nätverk. För ett välkänt linjärt-kvadratisk-gaussiskt kontrollproblem lyckas den approximerade neurala nätverksfunktionen erhållen med stokastisk gradient nedstigningsalgoritm att reproducera till formen av den teoretiska optimala styrfunktionen och tillämpning av olika typer av algoritmer för maskininlärning optimering ger en ganska nära noggrannhet med avseende på deras motsvarande empiriska värdefunktion. Vidare är det visat att noggrannheten och stabiliteten hos maskininlärning simetrationen kan förbättras genom att öka storleken på minibatch och tillämpa ett finare diskretiseringsschema. Dessa resultat tyder på effektiviteten och lämpligheten av att tillämpa maskininlärningsalgoritmen för stokastisk optimal styrning.