Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 755)
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Table of contents (7 chapters)
Keywords
About this book
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.
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)
Authors and Affiliations
Bibliographic Information
Book Title: Machine Learning
Book Subtitle: Discriminative and Generative
Authors: Tony Jebara
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4419-9011-2
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2004
Hardcover ISBN: 978-1-4020-7647-3Published: 31 December 2003
Softcover ISBN: 978-1-4613-4756-9Published: 27 September 2012
eBook ISBN: 978-1-4419-9011-2Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XVII, 200
Topics: Artificial Intelligence, Statistics, general, Information Storage and Retrieval, Computer Imaging, Vision, Pattern Recognition and Graphics