Machine Learning: Discriminative and GenerativeAuthor: Tony Jebara Published by Springer US ISBN: 978-1-4613-4756-9 DOI: 10.1007/978-1-4419-9011-2Table of Contents:Introduction Generative Versus Discriminative Learning Maximum Entropy Discrimination Extensions to MED Latent Discrimination Conclusion Appendix List of Figures -- List of Tables -- Preface -- Acknowledgments -- 1. Introduction -- 2. Generative Versus Discriminative Learning -- 3. Maximum Entropy Discrimination -- 4. Extensions To MED -- 5. Latent Discrimination -- 6. Conclusion -- 7. Appendix -- Index 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