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.