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Machine Learning

Discriminative and Generative

  • Tony┬áJebara

Part of the The International Series in Engineering and Computer Science book series (SECS, volume 755)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Tony Jebara
    Pages 1-16
  3. Tony Jebara
    Pages 61-98
  4. Tony Jebara
    Pages 99-130
  5. Tony Jebara
    Pages 131-169
  6. Tony Jebara
    Pages 171-177
  7. Tony Jebara
    Pages 179-197
  8. Back Matter
    Pages 199-200

About this book

Introduction

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.

Keywords

Extension computer science learning machine learning

Authors and affiliations

  • Tony┬áJebara
    • 1
  1. 1.Columbia UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-9011-2
  • Copyright Information Kluwer Academic Publishers 2004
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-4756-9
  • Online ISBN 978-1-4419-9011-2
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
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