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Kernel Methods and Machine Learning

Name: Kernel Methods and Machine Learning
Brand: Cambridge University Press
SKU: 02050759
Price: 124.59 EUR
Availability: InStock
Author: S. Y. Kung
ISBN: 9781107024960

S. Y. Kung

Language

English

Book Hardback

Libristo code: 02050759

Publishers Cambridge University Press, April 2014

Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and... Full description

Libristo code: 02050759

305 b

124.59 € VAT included

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Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.