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Bibliographies thématiques / Machine Learning Model Robustness / Livres

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Livres sur le sujet « Machine Learning Model Robustness »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 11 mars 2023

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1

Mohamed, Khaled Salah. Machine Learning for Model Order Reduction. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75714-8.

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Subrahmanian, V. S., Chiara Pulice, James F. Brown et Jacob Bonen-Clark. A Machine Learning Based Model of Boko Haram. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60614-5.

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Sturm, Jürgen. Approaches to Probabilistic Model Learning for Mobile Manipulation Robots. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013.

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4

Widjanarko, Bambang. Pengembangan model model machine learning ketahanan pangan melalui pembentukan zona musim (ZOM) suatu wilayah : Laporan akhir hibah kompetitif penelitian sesuai prioritas nasional tahun I. Surabaya : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, Institut Teknologi Sepuluh Nopember, 2010.

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5

Adversarial Robustness for Machine Learning Models. Elsevier Science & Technology Books, 2022.

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6

Adversarial Robustness for Machine Learning Models. Elsevier Science & Technology, 2022.

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7

Adversarial Robustness for Machine Learning. Elsevier, 2023. http://dx.doi.org/10.1016/c2020-0-01078-9.

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8

Machine Learning Algorithms : Adversarial Robustness in Signal Processing. Springer International Publishing AG, 2022.

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9

Winn, John Michael. Model-Based Machine Learning. Taylor & Francis Group, 2021.

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10

Mohamed, Khaled Salah. Machine Learning for Model Order Reduction. Springer, 2019.

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11

Mohamed, Khaled Salah. Machine Learning for Model Order Reduction. Springer, 2018.

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12

Golden, Richard. Statistical Machine Learning : A Model-Based Approach. Taylor & Francis Group, 2020.

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13

Golden, Richard. Statistical Machine Learning : A Model-Based Approach. Taylor & Francis Group, 2020.

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14

Pulice, Chiara, Jacob Bonen-Clark, Geert Kuiper, James F. Brown et V. S. Subrahmanian. Machine Learning Based Model of Boko Haram. Springer International Publishing AG, 2021.

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15

Pulice, Chiara, Jacob Bonen-Clark, James F. Brown et V. S. Subrahmanian. Machine Learning Based Model of Boko Haram. Springer International Publishing AG, 2020.

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16

Golden, Richard. Statistical Machine Learning : A Model-Based Approach. Taylor & Francis Group, 2020.

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17

Golden, Richard. Statistical Machine Learning : A Model-Based Approach. Taylor & Francis Group, 2020.

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18

Golden, Richard. Statistical Machine Learning : A Model-Based Approach. Taylor & Francis Group, 2020.

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19

Kullman, Inger. Basics of Machine Learning : Train a Machine Learning Model : Artificial Intelligence a Modern. Independently Published, 2021.

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20

Ok, DoKyeong. A study of model-based average reward reinforcement learning. 1996.

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21

Ok, DoKyeong. A study of model-based average reward reinforcement learning. 1996.

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22

Schonert, Elwood. Markov Model for Beginners Guidebook : Machine Learning Model Training : Techniques to Evaluate Markov Model. Independently Published, 2021.

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23

Gradillas, Royce. Machine Learning Algorithms : How to Choose the Right Kind of Machine Learning Model : An Amateur Software Developer. Independently Published, 2021.

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24

Nandi, Anirban, et Aditya Kumar Pal. Interpreting Machine Learning Models : Learn Model Interpretability and Explainability Methods. Apress L. P., 2022.

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25

Keith, Mark J. Machine Learning in Python : From Data Collection to Model Deployment. MyEducator, Inc., 2022.

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26

Chan, Chee Seng, Qiang Yang et Lixin Fan. Digital Watermarking for Machine Learning Model : Techniques, Protocols and Applications. Springer, 2023.

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27

Building Machine Learning Pipelines : Automating Model Life Cycles with TensorFlow. O'Reilly Media, Incorporated, 2020.

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28

Amirghodsi, Siamak, Meenakshi Rajendran, Broderick Hall et Shuen Mei. Apache Spark 2.x Machine Learning Cookbook : Over 100 recipes to simplify machine learning model implementations with Spark. Packt Publishing - ebooks Account, 2017.

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29

Rao, Dattaraj. Keras to Kubernetes : The Journey of a Machine Learning Model to Production. Wiley & Sons, Incorporated, John, 2019.

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30

Rao, Dattaraj. Keras to Kubernetes : The Journey of a Machine Learning Model to Production. Wiley & Sons, Limited, John, 2019.

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31

Rao, Dattaraj. Keras to Kubernetes : The Journey of a Machine Learning Model to Production. Wiley & Sons, Incorporated, John, 2019.

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32

Rao, Dattaraj. Keras to Kubernetes : The Journey of a Machine Learning Model to Production. Wiley & Sons, Incorporated, John, 2019.

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33

Wang, Guanhua. Distributed Machine Learning with Python : Accelerating Model Training and Serving with Distributed Systems. Packt Publishing, Limited, 2022.

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34

Yu, Jean, Kai Yu et Heli Helskyaho. Machine Learning for Oracle Database Professionals : Deploying Model-Driven Applications and Automation Pipelines. Apress L. P., 2021.

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35

Sturm, Jürgen. Approaches to Probabilistic Model Learning for Mobile Manipulation Robots. Springer, 2013.

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36

Sturm, Jürgen. Approaches to Probabilistic Model Learning for Mobile Manipulation Robots. Springer Berlin / Heidelberg, 2015.

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37

Farhan, Muhammad, Noor Zaman Jhanjhi, Muhammad Umer, Rana M. Amir Latif, Mamoona Humayun et Syed Jawad Hussain. A Smart Agriculture Land Suitability Detection Model Using Machine Learning with Google Earth Engine. Eliva Press, 2020.

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38

Munn, Michael, Sara Robinson et Valliappa Lakshmanan. Machine Learning Design Patterns : Solutions to Common Challenges in Data Preparation, Model Building, and MLOps. O'Reilly Media, Incorporated, 2020.

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39

Jena, Om Prakash, Alok Ranjan Tripathy, Brojo Kishore Mishra et Ahmed A. Elngar, dir. Augmented Intelligence : Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150404011220301.

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Blockchain, whether public or private, is capable enough to maintain the integrity of transactions by decentralizing the records for users. Many IoT companies are using blockchain technology to make the world a better-connected place. Businesses and researchers are exploring ways to make this technology increasingly efficient for IoT services. This volume presents the recent advances in these two technologies. Chapters explain the fundamentals of Blockchain and IoT, before explaining how these technologies, when merged together, provide a transparent, reliable, and secure model for data processing by intelligent devices in various domains. Readers will be able to understand how these technologies are making an impact on healthcare, supply chain management and electronic voting, to give a few examples. The 10 peer-reviewed book chapters have been contributed by scholars, researchers, academicians, and engineering professionals, and provide a comprehensive yet easily digestible update on Blockchain on IoT technology.

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40

Aronson, David, et Timothy Masters. Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments : Developing Predictive-Model-Based Trading Systems Using TSSB. CreateSpace Independent Publishing Platform, 2013.

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41

Islam, Johirul. Machine Learning Model Serving Patterns and Best Practices : A Definitive Guide to Deploying, Monitoring, and Providing Accessibility to ML Models in Production. Packt Publishing, Limited, 2022.

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42

Learning, Josh Hugh. Python for Beginners : A Step by Step Guide to Python Programming, Data Science, and Predictive Model. a Practical Introduction to Machine Learning with Python. Independently Published, 2019.

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43

On Data Mining in Context : Cases, Fusion and Evaluation. Leiden, The Netherlands : Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University, 2010.

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44

Shaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.

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Does your brain replay your recent life experiences while you are resting? An open question in neuroscience is which events does our brain replay and is there any correlation between the replay and duration of the event? In this study I tried to investigate this question by using Magnetoencephalography data from an active listening experiment. Magnetoencephalography (MEG) is a non-invasive neuroimaging technique used to study the brain activity and understand brain dynamics in perception and cognitive tasks particularly in the fields of speech and hearing. It records the magnetic field generated in our brains to detect the brain activity. I build a machine learning pipeline which uses part of the experiment data to learn the sound patterns and then predicts the presence of sound in the later part of the recordings in which the participants were made to sit idle and no sound was fed. The aim of the study of test replay of learned sound sequences in the post listening period. I have used classification scheme to identify patterns if MEG responses to different sound sequences in the post task period. The study concluded that the sound sequences can be identified and distinguished above theoretical chance level and hence proved the validity of our classifier. Further, the classifier could predict the sound sequences in the post-listening period with very high probability but in order to validate the model results on post listening period, more evidence is needed.

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45

Hanson, Stephen José, Michael J. Kearns, Thomas Petsche et Ronald L. Rivest, dir. Computational Learning Theory and Natural Learning Systems, Volume 2. The MIT Press, 1994. http://dx.doi.org/10.7551/mitpress/2029.001.0001.

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Computational learning theory, neural networks, and AI machine learning appear to be disparate fields; in fact they have the same goal: to build a machine or program that can learn from its environment. Accordingly, many of the papers in this volume deal with the problem of learning from examples. In particular, they are intended to encourage discussion between those trying to build learning algorithms (for instance, algorithms addressed by learning theoretic analyses are quite different from those used by neural network or machine-learning researchers) and those trying to analyze them. The first section provides theoretical explanations for the learning systems addressed, the second section focuses on issues in model selection and inductive bias, the third section presents new learning algorithms, the fourth section explores the dynamics of learning in feedforward neural networks, and the final section focuses on the application of learning algorithms. Bradford Books imprint

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46

Gureckis, Todd M., et Bradley C. Love. Computational Reinforcement Learning. Sous la direction de Jerome R. Busemeyer, Zheng Wang, James T. Townsend et Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.5.

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Reinforcement learning (RL) refers to the scientific study of how animals and machines adapt their behavior in order to maximize reward. The history of RL research can be traced to early work in psychology on instrumental learning behavior. However, the modern field of RL is a highly interdisciplinary area that lies that the intersection of ideas in computer science, machine learning, psychology, and neuroscience. This chapter summarizes the key mathematical ideas underlying this field including the exploration/exploitation dilemma, temporal-difference (TD) learning, Q-learning, and model-based versus model-free learning. In addition, a broad survey of open questions in psychology and neuroscience are reviewed.

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Sekhon, Jasjeet. The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods. Sous la direction de Janet M. Box-Steffensmeier, Henry E. Brady et David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0011.

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This article presents a detailed discussion of the Neyman-Rubin model of causal inference. Additionally, it describes under what conditions ‘matching’ approaches can lead to valid inferences, and what kinds of compromises sometimes have to be made with respect to generalizability to ensure valid causal inferences. Moreover, the article summarizes Mill's first three canons and shows the importance of taking chance into account and comparing conditional probabilities when chance variations cannot be ignored. The significance of searching for causal mechanisms is often overestimated by political scientists and this sometimes leads to an underestimate of the importance of comparing conditional probabilities. The search for causal mechanisms is probably especially useful when working with observational data. Machine learning algorithms can be used against the matching problem.

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48

LAND.TECHNIK 2022. VDI Verlag, 2022. http://dx.doi.org/10.51202/9783181023952.

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INHALT Electrical Agricultural Machines Structuring of electrified agricultural machine systems – Diversity of solutions and analysis methods .....1 GridCON2 – Development of a Cable Drum Vehicle Concept to Power 1MW Fully Electric Agricultural Swarms ..... 11 GridCON Swarm – Development of a Grid Connected Fully Autonomous Agricultural Production System ..... 17 Fully electric Tractor with 1000 kWh battery capacity ..... 23 Soil and Modelling The Integration of a Scientific Soil Compaction Risk Indicator (TERRANIMO) into a Holistic Tractor and Implement Optimization System (CEMOS) .....29 Identification of draft force characteristics for a tillage tine with variable geometry ..... 37 Calibration of soil models within the Discrete Element Method (DEM) ..... 45 Automation and Optimization of Working Speed and Depth in Agricultural Soil Tillage with a Model Predictive Control based on Machine Learning ..... 55 Synchronising machine adjustments of combine harvesters for higher fleet performance ..... 65 A generic approach to bridge the gap between route optimization and motion planning for specific guidance points o...

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49

Makatjane, Katleho, et Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.

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Samuelsson, Christer. Statistical Methods. Sous la direction de Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0019.

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Statistical methods now belong to mainstream natural language processing. They have been successfully applied to virtually all tasks within language processing and neighbouring fields, including part-of-speech tagging, syntactic parsing, semantic interpretation, lexical acquisition, machine translation, information retrieval, and information extraction and language learning. This article reviews mathematical statistics and applies it to language modelling problems, leading up to the hidden Markov model and maximum entropy model. The real strength of maximum-entropy modelling lies in combining evidence from several rules, each one of which alone might not be conclusive, but which taken together dramatically affect the probability. Maximum-entropy modelling allows combining heterogeneous information sources to produce a uniform probabilistic model where each piece of information is formulated as a feature. The key ideas of mathematical statistics are simple and intuitive, but tend to be buried in a sea of mathematical technicalities. Finally, the article provides mathematical detail related to the topic of discussion.

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