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Kernel Methods and Machine Learning

Kernel Methods and Machine Learning

by S. Y. Kung
Hardback
Publication Date: 17/04/2014

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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.
ISBN:
9781107024960
9781107024960
Category:
Pattern recognition
Format:
Hardback
Publication Date:
17-04-2014
Language:
English
Publisher:
Cambridge University Press
Country of origin:
United Kingdom
Pages:
572
Dimensions (mm):
252x176x29mm
Weight:
1.35kg

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