Information Theory and Statistical Learning PDF Download
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Author: Frank Emmert-Streib
Publisher: Springer Science & Business Media
ISBN: 0387848150
Category : Computers
Languages : en
Pages : 443
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Book Description
This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.
Author: Frank Emmert-Streib
Publisher: Springer Science & Business Media
ISBN: 0387848150
Category : Computers
Languages : en
Pages : 443
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Book Description
This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.
Author: David J. C. MacKay
Publisher: Cambridge University Press
ISBN: 9780521642989
Category : Computers
Languages : en
Pages : 694
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Book Description
Table of contents
Author: Vladimir Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475732643
Category : Mathematics
Languages : en
Pages : 324
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Book Description
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Author: Jose C. Principe
Publisher: Springer Science & Business Media
ISBN: 1441915702
Category : Computers
Languages : en
Pages : 538
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Book Description
This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.
Author: Solomon Kullback
Publisher: Courier Corporation
ISBN: 0486142043
Category : Mathematics
Languages : en
Pages : 460
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Book Description
Highly useful text studies logarithmic measures of information and their application to testing statistical hypotheses. Includes numerous worked examples and problems. References. Glossary. Appendix. 1968 2nd, revised edition.
Author: Miguel R. D. Rodrigues
Publisher: Cambridge University Press
ISBN: 1108427138
Category : Computers
Languages : en
Pages : 561
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Book Description
The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.
Author: Olivier Catoni
Publisher: Springer
ISBN: 3540445072
Category : Mathematics
Languages : en
Pages : 278
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Book Description
Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously "wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade the book: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools, that will stimulate further studies and results.
Author: Imre Csiszár
Publisher: Now Publishers Inc
ISBN: 9781933019055
Category : Computers
Languages : en
Pages : 128
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Book Description
Information Theory and Statistics: A Tutorial is concerned with applications of information theory concepts in statistics, in the finite alphabet setting. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an "information geometry" background. Also, an introduction is provided to the theory of universal coding, and to statistical inference via the minimum description length principle motivated by that theory. The tutorial does not assume the reader has an in-depth knowledge of Information Theory or statistics. As such, Information Theory and Statistics: A Tutorial, is an excellent introductory text to this highly-important topic in mathematics, computer science and electrical engineering. It provides both students and researchers with an invaluable resource to quickly get up to speed in the field.
Author: Jorma Rissanen
Publisher: Springer Science & Business Media
ISBN: 0387688129
Category : Mathematics
Languages : en
Pages : 145
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Book Description
No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial.
Author: Sumio Watanabe
Publisher: Cambridge University Press
ISBN: 0521864674
Category : Computers
Languages : en
Pages : 295
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Book Description
Sure to be influential, Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.
