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£180 today · previous high £180 · all-time low £144

Springer Information Theoretic Learning - Renyi's Entropy

Amazon

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£179.99 £140.86 £149.40 £157.94 £166.47 £175.01 £183.55 26 July 2026 15 August 2026 04 September 2026 24 September 2026 14 October 2026
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Description

Explore the fundamentals of Information Theoretic Learning (ITL) with this specialized text from Springer. Part of the Information Science and Statistics series, this book provides the first cohesive treatment of ITL algorithms designed to adapt both linear and nonlinear learning machines. Readers will examine how these algorithms function within both supervised and unsupervised learning paradigms. The text offers a direct comparison of ITL algorithm performance against second order counterparts across various applications, making it a valuable resource for those studying advanced machine learning and statistical theory.

Key Features

This Springer publication provides the first cohesive treatment of ITL algorithms for learning machines.

Learn to adapt both linear and nonlinear learning machines through supervised and unsupervised paradigms.

Compare the performance of ITL algorithms against second order counterparts in many applications.

Part of the professional Information Science and Statistics book series for advanced study.

Covers essential concepts regarding Renyi's Entropy and kernel perspectives in machine learning.

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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