9781107024960book CUP/KUN-L1 November 9, 2013 17:09 Page-i
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 analyzing which factors to consider in tackling a
given problem, enabling readers to improve specifically designed learning algorithms
and build models for new applications or develop efficient techniques suitable for green
machine learning technologies.
Numerous real-world examples and over 200 problems, several of which are
M
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
processing, sensor networks, bioinformatics, data mining, and machine learning.
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