Learning In Non Stationary Environments

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Learning In Non Stationary Environments

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Author : Springer
language : en
Publisher:
Release Date : 2012-04-01

Learning In Non Stationary Environments written by Springer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-01 with categories.

Learning In Non Stationary Environments

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Author : Moamar Sayed-Mouchaweh
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-04-13

Learning In Non Stationary Environments written by Moamar Sayed-Mouchaweh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-13 with Technology & Engineering categories.

Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.

Machine Learning In Non Stationary Environments

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Author : Masashi Sugiyama
language : en
Publisher: MIT Press
Release Date : 2012

Machine Learning In Non Stationary Environments written by Masashi Sugiyama and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.

Dealing with non-stationarity is one of modem machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity.

Machine Learning In Non Stationary Environments

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Author : Motoaki Kawanabe
language : en
Publisher:
Release Date :

Machine Learning In Non Stationary Environments written by Motoaki Kawanabe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity.

Special Issue Adaptive And Online Learning In Non Stationary Environments

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Author : Edwin Lughofer
language : en
Publisher:
Release Date : 2015

Special Issue Adaptive And Online Learning In Non Stationary Environments written by Edwin Lughofer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.

Adapting Machine Learning To Non Stationary Environments

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Author : Wintheiser Donnie
language : en
Publisher:
Release Date : 2023-04-04

Adapting Machine Learning To Non Stationary Environments written by Wintheiser Donnie and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-04 with Computers categories.

Machine learning stimulates a broad range of computational methods that exploit experience, which typically takes the form of electronic data, to make profitable decisions or accurate predictions. To date, the machine learning models have been applied to extensive application domains across diverse fields, including but not limited to computer vision [1, 2, 3], natural language processing [4, 5, 6], robotic control [7, 8], and cyber security [9, 10, 11].

Reinforcement Learning

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Author : Richard S. Sutton
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Reinforcement Learning written by Richard S. Sutton and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.

Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.

Markov Decision Processes

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Author : Martin L. Puterman
language : en
Publisher: John Wiley & Sons
Release Date : 2014-08-28

Markov Decision Processes written by Martin L. Puterman and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-28 with Mathematics categories.

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential." —Zentralblatt fur Mathematik ". . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes." —Journal of the American Statistical Association

Multiagent Learning In Non Stationary Environments

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Author : Michael Weinberg
language : en
Publisher:
Release Date : 2006

Multiagent Learning In Non Stationary Environments written by Michael Weinberg and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.

Bandit Algorithms

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Author : Tor Lattimore
language : en
Publisher: Cambridge University Press
Release Date : 2020-07-16

Bandit Algorithms written by Tor Lattimore and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-16 with Business & Economics categories.

A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.