Relevant bibliographies by topics / Machine learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Machine learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Machine learning"

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M. Brandao, Iago, and Cesar da Costa. "FAULT DIAGNOSIS OF ROTARY MACHINES USING MACHINE LEARNING." Eletrônica de Potência 27, no. 03 (September 22, 2022): 1–8. http://dx.doi.org/10.18618/rep.2022.3.0013.

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Naeini, Ehsan Zabihi, and Kenton Prindle. "Machine learning and learning from machines." Leading Edge 37, no. 12 (December 2018): 886–93. http://dx.doi.org/10.1190/tle37120886.1.

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Sabeti, Behnam, Hossein Abedi Firouzjaee, Reza Fahmi, Saeid Safavi, Wenwu Wang, and Mark D. Plumbley. "Credit Risk Rating Using State Machines and Machine Learning." International Journal of Trade, Economics and Finance 11, no. 6 (December 2020): 163–68. http://dx.doi.org/10.18178/ijtef.2020.11.6.683.

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Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed state machine is used to convert historical user data to a credit score which generates a data-set for training supervised models. We have explored several classification models in our experiments and illustrated the effectiveness of our modeling approach.
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Trott, David. "Deceiving Machines: Sabotaging Machine Learning." CHANCE 33, no. 2 (April 2, 2020): 20–24. http://dx.doi.org/10.1080/09332480.2020.1754067.

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Siddique, Shumaila. "Machine Learning and Cryptography." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 2540–45. http://dx.doi.org/10.5373/jardcs/v12sp7/20202387.

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Charpentier, Arthur, Emmanuel Flachaire, and Antoine Ly. "Econometrics and Machine Learning." Economie et Statistique / Economics and Statistics, no. 505d (April 11, 2019): 147–69. http://dx.doi.org/10.24187/ecostat.2018.505d.1970.

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Mor, Laksanya. "Introduction to Machine Learning." International Journal of Science and Research (IJSR) 11, no. 3 (March 5, 2022): 1522–25. http://dx.doi.org/10.21275/sr22328110600.

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Lewis, Ted G., and Peter J. Denning. "Learning machine learning." Communications of the ACM 61, no. 12 (November 20, 2018): 24–27. http://dx.doi.org/10.1145/3286868.

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Rasi, Mr Ajmal, Dr Rajasimha A. Makram, and Ms Shilpa Das. "Topic Detection using Machine Learning." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1433–36. http://dx.doi.org/10.31142/ijtsrd14272.

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Mudiraj, Nakkala Srinivas. "Detecting Phishing using Machine Learning." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 488–90. http://dx.doi.org/10.31142/ijtsrd23755.

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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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Books on the topic "Machine learning"

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Zhou, Zhi-Hua. Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3.

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Jung, Alexander. Machine Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8193-6.

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Mitchell, Tom M., Jaime G. Carbonell, and Ryszard S. Michalski. Machine Learning. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2279-5.

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Fernandes de Mello, Rodrigo, and Moacir Antonelli Ponti. Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94989-5.

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Bell, Jason. Machine Learning. Indianapolis, IN, USA: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781119183464.

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Huang, Kaizhu, Haiqin Yang, Irwin King, and Michael Lyu. Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79452-3.

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Jebara, Tony. Machine Learning. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9011-2.

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Vorobeychik, Yevgeniy, and Murat Kantarcioglu. Adversarial Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01580-9.

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Chen, Zhiyuan, and Bing Liu. Lifelong Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01581-6.

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Tsihrintzis, George A., Dionisios N. Sotiropoulos, and Lakhmi C. Jain, eds. Machine Learning Paradigms. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-94030-4.

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Book chapters on the topic "Machine learning"

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Hassan, Shabbir. "Machine learning vs. neutrosophic machine learning." In Soft Computing and Machine Learning, 102–35. Boca Raton: CRC Press, 2025. https://doi.org/10.1201/9781003606055-5.

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Wehenkel, Louis A. "Machine Learning." In Automatic Learning Techniques in Power Systems, 99–144. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5451-6_5.

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Cios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. "Machine Learning." In Data Mining Methods for Knowledge Discovery, 229–308. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_6.

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Schuld, Maria, and Francesco Petruccione. "Machine Learning." In Quantum Science and Technology, 21–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96424-9_2.

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Dinsmore, Thomas W. "Machine Learning." In Disruptive Analytics, 169–98. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1311-7_8.

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Yao, Xin, and Yong Liu. "Machine Learning." In Search Methodologies, 477–517. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6940-7_17.

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Ben-Ari, Mordechai, and Francesco Mondada. "Machine Learning." In Elements of Robotics, 221–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62533-1_14.

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Kwok, James T., Zhi-Hua Zhou, and Lei Xu. "Machine Learning." In Springer Handbook of Computational Intelligence, 495–522. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43505-2_29.

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Cobia, Derin. "Machine Learning." In Encyclopedia of Clinical Neuropsychology, 2058–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_9058.

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Zielesny, Achim. "Machine Learning." In Intelligent Systems Reference Library, 221–380. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21280-2_4.

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Conference papers on the topic "Machine learning"

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Kozhenkov, A., E. Z. Naeini, and K. Prindle. "Machine Learning and Learning from Machines." In Progress’19. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201953052.

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Chaudhuri, Arjun, Jonti Talukdar, and Krishnendu Chakrabarty. "Machine Learning for Testing Machine-Learning Hardware." In ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3508352.3561121.

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"Machine learning." In 2015 International Symposium on Advanced Computing and Communication (ISACC). IEEE, 2015. http://dx.doi.org/10.1109/isacc.2015.7377313.

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Mitrofanova, A. S., and G. V. Komlev. "Machine learning." In ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ. НИЦ «Л-Журнал», 2018. http://dx.doi.org/10.18411/lj-11-2018-180.

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"Machine Learning." In 2019 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2019. http://dx.doi.org/10.1109/iwssip.2019.8787334.

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Young, Ramsey, and Jonathan Ringenberg. "Machine Learning." In SIGCSE '19: The 50th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3287324.3293806.

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Mohammed, Hadi, Ibrahim A. Hameed, and Razak Seidu. "Machine learning." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3208235.

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"Machine Learning." In 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2022. http://dx.doi.org/10.1109/iwssip55020.2022.9854395.

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Jordan, Michael I. "Machine learning." In TURC 2018: ACM Turing Celebration Conference - China. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3210713.3210718.

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Han, DongYeob. "Crack detection of UAV concrete surface images." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2525174.

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Reports on the topic "Machine learning"

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Vesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492563.

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Valiant, L. G. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada283386.

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Chase, Melissa P. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada223732.

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Kagie, Matthew J., and Park Hays. FORTE Machine Learning. Office of Scientific and Technical Information (OSTI), August 2016. http://dx.doi.org/10.2172/1561828.

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Lin, Youzuo, Shihang Feng, and Esteban Rougier. Machine Learning Tutorial. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1876777.

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Vassilev, Apostol. Adversarial Machine Learning:. Gaithersburg, MD: National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.100-2e2023.

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Kelly, Bryan, and Dacheng Xiu. Financial Machine Learning. Cambridge, MA: National Bureau of Economic Research, July 2023. http://dx.doi.org/10.3386/w31502.

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Caplin, Andrew, Daniel Martin, and Philip Marx. Modeling Machine Learning. Cambridge, MA: National Bureau of Economic Research, October 2022. http://dx.doi.org/10.3386/w30600.

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Christie, Lorna. Interpretable machine learning. Parliamentary Office of Science and Technology, October 2020. http://dx.doi.org/10.58248/pn633.

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Abstract:
Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives an overview of ML and its role in decision-making. It examines the challenges of understanding how a complex ML system has reached its output, and some of the technical approaches to making ML easier to interpret. It also gives a brief overview of some of the proposed tools for making ML systems more accountable.
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Vassilev, Apostol. Adversarial Machine Learning:. Gaithersburg, MD: National Institute of Standards and Technology, 2025. https://doi.org/10.6028/nist.ai.100-2e2025.

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