Kernel Methods and Machine Learning

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.

Author: S. Y. Kung
Publisher: Cambridge University Press
Published: 04/17/2014
Pages: 572
Binding Type: Hardcover
Weight: 3.00lbs
Size: 10.00h x 6.90w x 1.20d
ISBN13: 9781107024960
ISBN10: 110702496X
BISAC Categories:
- Computers | Artificial Intelligence | Computer Vision & Pattern Recognit