£144.27

Springer Information Theoretic Learning - Renyi's Entropy Book

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Description

Expand your knowledge of machine learning with Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives. Published by Springer as part of the Information Science and Statistics series, this text provides the first cohesive treatment of ITL algorithms. It is designed to help readers understand how to adapt both linear and nonlinear learning machines across supervised and unsupervised paradigms. This book offers a deep dive into the comparative performance of ITL algorithms. By examining how these methods perform against second order counterparts in various applications, researchers and students can better grasp the practical advantages of information theoretic approaches. Whether you are studying computer science, AI, or advanced statistical methods, this resource serves as a foundational guide for mastering complex learning machine adaptations.

Key Features

Provides the first cohesive treatment of ITL algorithms for both linear and nonlinear learning machines.

Covers both supervised and unsupervised learning paradigms to broaden your technical understanding.

Includes detailed comparisons between ITL algorithms and their second order counterparts.

Explores diverse applications to demonstrate the practical utility of Renyi's Entropy and Kernel perspectives.

Part of the professional Information Science and Statistics series from Springer.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
27 May 2012
Listed Since
19 May 2012

Barcode

No barcode data available

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