Overview
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.
Full Product Details
Author: Tony Jebara
Publisher: Kluwer Academic Publishers
Imprint: Kluwer Academic Publishers
Edition: 2004 ed.
Volume: 755
Dimensions:
Width: 15.50cm
, Height: 1.40cm
, Length: 23.50cm
Weight: 4.616kg
ISBN: 9781402076473
ISBN 10: 1402076479
Pages: 200
Publication Date: 31 December 2003
Audience:
College/higher education
,
Professional and scholarly
,
Postgraduate, Research & Scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: In Print
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Reviews
"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)"
From the reviews: <p> This book aims to unite two powerful approaches in machine learning: generative and discriminative. a ] 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)
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)
