Description
Springer Machine Learning Discriminative and Generative by Tony Jebara
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. _x000D__x000D__x000D_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._x000D_ Table of contents : - _x000D_
- List of Figures. List of Tables. _x000D_
- Preface. Acknowledgments. _x000D_
- 1. Introduction. _x000D_
- 2. Generative Versus Discriminative Learning. _x000D_
- 3. Maximum Entropy Discrimination. _x000D_
- 4. Extensions To MED. _x000D_
- 5. Latent Discrimination. _x000D_
- 6. Conclusion. _x000D_
- 7. Appendix. _x000D_
- Index._x000D_