Information Theory And Statistical Learning
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Information Theory and Statistical Learning
Author | : Frank Emmert-Streib,Matthias Dehmer |
Publsiher | : Springer Science & Business Media |
Total Pages | : 443 |
Release | : 2009 |
Genre | : Computers |
ISBN | : 9780387848150 |
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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.
Information Theory Inference and Learning Algorithms
Author | : David J. C. MacKay |
Publsiher | : Cambridge University Press |
Total Pages | : 694 |
Release | : 2003-09-25 |
Genre | : Computers |
ISBN | : 0521642981 |
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Table of contents
The Nature of Statistical Learning Theory
Author | : Vladimir Vapnik |
Publsiher | : Springer Science & Business Media |
Total Pages | : 324 |
Release | : 2013-06-29 |
Genre | : Mathematics |
ISBN | : 9781475732641 |
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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.
Information Theoretic Learning
Author | : Jose C. Principe |
Publsiher | : Springer Science & Business Media |
Total Pages | : 538 |
Release | : 2010-04-06 |
Genre | : Computers |
ISBN | : 9781441915702 |
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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.
Information and Complexity in Statistical Modeling
Author | : Jorma Rissanen |
Publsiher | : Springer Science & Business Media |
Total Pages | : 145 |
Release | : 2007-12-15 |
Genre | : Mathematics |
ISBN | : 9780387688121 |
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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.
An Elementary Introduction to Statistical Learning Theory
Author | : Sanjeev Kulkarni,Gilbert Harman |
Publsiher | : John Wiley & Sons |
Total Pages | : 267 |
Release | : 2011-06-09 |
Genre | : Mathematics |
ISBN | : 9781118023464 |
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A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.
Statistical Learning Theory and Stochastic Optimization
Author | : Olivier Catoni |
Publsiher | : Springer |
Total Pages | : 278 |
Release | : 2004-08-30 |
Genre | : Mathematics |
ISBN | : 9783540445074 |
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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.
Statistical Learning Theory
Author | : Vladimir Naumovich Vapnik |
Publsiher | : Wiley-Interscience |
Total Pages | : 778 |
Release | : 1998-09-30 |
Genre | : Mathematics |
ISBN | : UOM:39076002704257 |
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A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.