TY  - DATA
AU  - Kung, S. Y.
TI  - Kernel Methods and Machine Learning
SN  - 9781139176224
U1  - 006.310 151 252 
PY  - 2014///
CY  - Cambridge
PB  - : Cambridge University Press
KW  - Communications And Signal Processing
KW  - Engineering
KW  - Machine Learning
N2  - 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
UR  - https://doi.org/10.1017/CBO9781139176224
ER  -