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Kernel Methods and Machine Learning by S. Y. Kung (Princeton University, New Jersey) at Abbey's Bookshop,
Kernel Methods and Machine Learning
— —
S. Y. Kung (Princeton University, New Jersey)

Kernel Methods and Machine Learning

S. Y. Kung (Princeton University, New Jersey)

$159.95

Hardback

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English
Cambridge University Press
17 April 2014
Machine learning; Pattern recognition
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.
By:  
Imprint:   Cambridge University Press
Country of Publication:   United Kingdom
Dimensions:   Height: 252mm,  Width: 176mm,  Spine: 29mm
Weight:   1.350kg
ISBN:   9781107024960
ISBN 10:   110702496X
Pages:   572
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

S. Y. Kung is a Professor in the Department of Electrical Engineering at Princeton University. His research areas include VLSI array/parallel processors, system modeling and identification, wireless communication, statistical signal processing, multimedia processing, sensor networks, bioinformatics, data mining and machine learning.

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