Information Theoretic Learning
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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 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
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 Theoretic Learning
Author | : Jose C. Principe |
Publsiher | : Springer |
Total Pages | : 448 |
Release | : 2010-04-15 |
Genre | : Computers |
ISBN | : 1441915699 |
Download Information Theoretic Learning Book in PDF, Epub and Kindle
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.
Robust Recognition via Information Theoretic Learning
Author | : Ran He,Baogang Hu,Xiaotong Yuan,Liang Wang |
Publsiher | : Springer |
Total Pages | : 110 |
Release | : 2014-08-28 |
Genre | : Computers |
ISBN | : 9783319074160 |
Download Robust Recognition via Information Theoretic Learning Book in PDF, Epub and Kindle
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 Theoretic Methods in Data Science
Author | : Miguel R. D. Rodrigues,Yonina C. Eldar |
Publsiher | : Cambridge University Press |
Total Pages | : 561 |
Release | : 2021-04-08 |
Genre | : Computers |
ISBN | : 9781108427135 |
Download Information Theoretic Methods in Data Science Book in PDF, Epub and Kindle
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
Author | : Gustavo Deco,Dragan Obradovic |
Publsiher | : Springer Science & Business Media |
Total Pages | : 265 |
Release | : 2012-12-06 |
Genre | : Computers |
ISBN | : 9781461240167 |
Download An Information Theoretic Approach to Neural Computing Book in PDF, Epub and Kindle
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.
Information Theoretic Learning
Author | : Anonim |
Publsiher | : Springer |
Total Pages | : 538 |
Release | : 2010 |
Genre | : Electronic Book |
ISBN | : 1441915737 |
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