Kernel Methods and Machine Learning

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

ATLAB

-based simulation exercises, make this an essential resource for graduate stu-

dents in computer science, and in electrical and biomedical engineering. It is also

a useful reference for researchers and practitioners in the field of machine learning.

Solutions to problems and additional resources are provided online for instructors.

S. Y. K

UNG

is a Professor in the Department of Electrical Engineering at Princeton Uni-

versity. His research areas include VLSI array/parallel processors, system modeling

and identification, wireless communication, statistical signal processing, multimedia