Book

Kernel methods and machine learning

Kung, S. Y. (Sun Yuan)

Overview

\"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\"-- Provided by publisher.

• Publisher: Cambridge University Press
• Subject: Support vector machines. / Machine learning. / Kernel functions. / COMPUTERS / Computer Vision & Pattern Recognition.
• ISBN: 9781107024960, 110702496X

Item info:

1 item available

1 item total in all locations

Holdings :

Call Number	Copies	Material	Location
Q325.5.K86 2014	1	BOOK	AUTOSTORE