Kernel Methods and Machine Learning - Couverture rigide

Kung, S. Y.

Couverture rigide

ISBN 10 : 110702496X ISBN 13 : 9781107024960

Editeur : Cambridge University Press, 2014

Synopsis

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.

A propos du livre

À propos de l'auteur

S. Y. Kung is a Professor in the Department of Electrical Engineering at Princeton University. 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.

Détails de l'édition

• Éditeur: Cambridge University Press
• Date d'édition: 2014
• Langue: anglais
• ISBN 10: 110702496X
• ISBN 13: 9781107024960
• Reliure: Relié
• Nombre de pages: 572
• Coordonnées du fabricant: non disponible
• Personne responsable: Avery; https://www.avery.fr/; ; chemin des peupliers 27; Dardilly; France