Machine Learning : Discriminative and Generative

Jebara, Tony

ISBN 10: 1402076479 / ISBN 13: 9781402076473

Published by Springer, 2003

About this Item

Seller Inventory # 2025112-n

Bibliographic Details

• Title: Machine Learning : Discriminative and Generative
• Publisher: Springer
• Publication Date: 2003
• Language: English
• ISBN 10: 1402076479
• ISBN 13: 9781402076473
• Binding: Hardcover
• Condition: New

About this title

Synopsis

Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning.

Machine Learning: Discriminative and Generative is designed for an audience composed of researchers & practitioners in industry and academia. The book is also suitable as a secondary text for graduate-level students in computer science and engineering.

Review

From the reviews:

"This book aims to unite two powerful approaches in machine learning: generative and discriminative. ... Researchers from the generative or discriminative schools will find this book a nice bridge to the other paradigm." (C. Andy Tsao, Mathematical Reviews, Issue 2005 k)