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Titlebook: Information Theoretic Learning; Renyi‘s Entropy and Jose C. Principe Book 2010 Springer-Verlag New York 2010 Correntropy.Information theor

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發(fā)表于 2025-3-21 16:29:12 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Information Theoretic Learning
副標(biāo)題Renyi‘s Entropy and
編輯Jose C. Principe
視頻videohttp://file.papertrans.cn/466/465738/465738.mp4
概述Includes supplementary material:
叢書名稱Information Science and Statistics
圖書封面Titlebook: Information Theoretic Learning; Renyi‘s Entropy and  Jose C. Principe Book 2010 Springer-Verlag New York 2010 Correntropy.Information theor
描述.This book presents the first cohesive treatment of Information Theoretic Learning (ITL) algorithms to adapt linear or nonlinear learning machines both in supervised or unsupervised paradigms. ITL is a framework where the conventional concepts of second order statistics (covariance, L2 distances, correlation functions) are substituted by scalars and functions with information theoretic underpinnings, respectively entropy, mutual information and correntropy. .ITL quantifies the stochastic structure of the data beyond second order statistics for improved performance without using full-blown Bayesian approaches that require a much larger computational cost. This is possible because of a non-parametric estimator of Renyi’s quadratic entropy that is only a function of pairwise differences between samples. The book compares the performance of ITL algorithms with the second order counterparts in many engineering and machine learning applications..Students, practitioners and researchers interested in statistical signal processing, computational intelligence, and machine learning will find in this book the theory to understand the basics, the algorithms to implement applications, and exciti
出版日期Book 2010
關(guān)鍵詞Correntropy; Information theoretic learning; Nongaussian signal processing; RKHS and information theory
版次1
doihttps://doi.org/10.1007/978-1-4419-1570-2
isbn_softcover978-1-4614-2585-4
isbn_ebook978-1-4419-1570-2Series ISSN 1613-9011 Series E-ISSN 2197-4128
issn_series 1613-9011
copyrightSpringer-Verlag New York 2010
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Algorithms for Entropy and Correntropy Adaptation with Applications to Linear Systems, algorithms for the mean square error criterion (MSE). Because the chapter assumes knowledge of adaptive filter design, readers unfamiliar with this topic should seek a textbook such as [332] or [253] for a review of fundamentals. But the treatment does not require an in-depth knowledge of this fiel
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Classification with EEC, Divergence Measures, and Error Bounds, for linear and nonlinear filtering (or regression) applications. However, the spectrum of engineering applications of adaptive systems is much broader than filtering or regression. Even looking at the subclass of supervised applications we have yet to deal with classification, which is an important
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Clustering with ITL Principles,anabe [330]. Although structure is a vague and difficult concept to quantify, structure fills the space with identifiable patterns that may be distinguishable macroscopically by the shape of the probability density function. Therefore, entropy and the concept of dissimilarity naturally form the foun
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A Reproducing Kernel Hilbert Space Framework for ITL, support vector machine (SVM) [63], kernel principal component analysis (KPCA) [289], and kernel Fisher discriminant analysis (KFDA) [219]. The common property of these methods is that they operate linearly, as they are explicitly expressed in terms of inner products in a transformed data space that
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