Kernel Methods and Machine Learning

Kung, S. Y.

ISBN 10: 110702496X / ISBN 13: 9781107024960

Verlag: Cambridge University Press, 2014

Beschreibung

Unread book in perfect condition. Bestandsnummer des Verkäufers 19977914

Bibliografische Details

Titel: Kernel Methods and Machine Learning

Verlag: Cambridge University Press

Erscheinungsdatum: 2014

Einband: Hardcover

Zustand: As New

Über diesen Titel

Inhaltsangabe:

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.

Über die Autorin bzw. den Autor:

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.