Kernel Methods and Machine Learning

von S. Y. Kung

616 Seiten, Hardcover

Kurzbeschreibung des Verlags

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.

• Themen: Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI) Mustererkennung
• ISBN: 9781107024960
• Sprache: Englisch
• Erscheinungsdatum: 17.04.2014
• Größe: 250 x 175 mm
• Verlag: Cambridge University Press