Information Theoretic Learning

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Information Theoretic Learning

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Author : Jose C. Principe
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-04-06

Information Theoretic Learning written by Jose C. Principe 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 2010-04-06 with Computers categories.

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 Theoretic Learning

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Author : Jose C. Principe
language : en
Publisher: Springer
Release Date : 2010-04-15

Information Theoretic Learning written by Jose C. Principe and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-04-15 with Computers categories.

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 Theoretic Learning

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Author :
language : en
Publisher: Springer
Release Date : 2010

Information Theoretic Learning written by and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.

Information Theoretic Learning

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Author : José C. Príncipe
language : en
Publisher:
Release Date : 2023

Information Theoretic Learning written by José C. Príncipe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Computer algorithms categories.

Information Theoretic Learning

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Author : Principe
language : en
Publisher: Wiley-Blackwell
Release Date : 2004-08-18

Information Theoretic Learning written by Principe and has been published by Wiley-Blackwell this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-08-18 with categories.

Information Theoretic Learning exploits information theoretic learning as a unifying principle to directly extract information from data, either in a self-organizing or supervised manner. Written by world-class experts in this field, the book discusses principles, algorithms, and applications, mirroring the development of the LMS algorithm for adaptive signal processing.

Robust Recognition Via Information Theoretic Learning

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Author : Ran He
language : en
Publisher: Springer
Release Date : 2014-08-28

Robust Recognition Via Information Theoretic Learning written by Ran He and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-28 with Computers categories.

This Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy. The authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency, the brief introduces the additive and multiplicative forms of half-quadratic optimization to efficiently minimize entropy problems and a two-stage sparse presentation framework for large scale recognition problems. It also describes the strengths and deficiencies of different robust measures in solving robust recognition problems.

Information Theory Inference And Learning Algorithms

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Author : David J. C. MacKay
language : en
Publisher: Cambridge University Press
Release Date : 2003-09-25

Information Theory Inference And Learning Algorithms written by David J. C. MacKay 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 2003-09-25 with Computers categories.

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Information Theory And Statistical Learning

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Author : Frank Emmert-Streib
language : en
Publisher: Springer Science & Business Media
Release Date : 2009

Information Theory And Statistical Learning written by Frank Emmert-Streib 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 2009 with Computers categories.

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 Theoretic Methods In Data Science

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Author : Miguel R. D. Rodrigues
language : en
Publisher: Cambridge University Press
Release Date : 2021-04-08

Information Theoretic Methods In Data Science written by Miguel R. D. Rodrigues 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 2021-04-08 with Computers categories.

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.

An Information Theoretic Approach To Neural Computing

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Author : Gustavo Deco
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

An Information Theoretic Approach To Neural Computing written by Gustavo Deco 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.

A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.