Machine learning : discriminative and generative
著者
書誌事項
Machine learning : discriminative and generative
（The Kluwer international series in engineering and computer science）
Springer Science+Business Media, c2004
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注記
"Originally published by Kluwer Academic Publishers in 2004, softcover reprint of the hardcover 1st edition 2004"--T.p. verso
Includes bibliographical references (p. [185]-197) and index
内容説明・目次
内容説明
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.
目次
- 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.
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