Kernel Methods and Machine Learning (Hardcover)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	April 2014
First published:	April 2014
Authors:	S.Y. Kung
Dimensions:	252 x 176 x 29mm (L x W x T)
Format:	Hardcover
Pages:	572
ISBN-13:	978-1-107-02496-0
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-107-02496-X
Barcode:	9781107024960

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