Machine Learning
Subtitle: Discriminative and Generative
By: by Tony Jebara
Series: The International Series in Engineering and Computer Science -- 755; International series in engineering and computer science -- 755.
Publisher: Springer US
Published: 2004
Place of publication: Boston, MA
Language: eng
Format: [electronic resource] :
Number of pages: 200
ISBN-13: 9781461347569; 9781441990112
Subjects: Information storage and retrieval systems; Computer vision; Statistics; Computer science; Artificial intelligence
Description: 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.
Notes: Online full text is restricted to subscribers.

Also available in print.

Mode of access: World Wide Web.
Table of contents: title: List of Figures; title: List of Tables; title: Preface; title: Acknowledgments; title: 1. Introduction; title: 2. Generative Versus Discriminative Learning; title: 3. Maximum Entropy Discrimination; title: 4. Extensions To MED; title: 5. Latent Discrimination; title: 6. Conclusion; title: 7. Appendix; title: Index.