Machine Learning: Discriminative and Generative
- ISBN-10
- 1441990119
- ISBN-13
- 9781441990112
- Series
- Machine Learning
- Category
- Computers
- Pages
- 200
- Language
- English
- Published
- 2012-12-06
- Publisher
- Springer Science & Business Media
- Author
- Tony Jebara
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.
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Other editions
-
- 2008-09-24
- 169 pages
- Ebook
- Springer Science & Business Media
-
- 2021-09-16
- 309 pages
- Ebook
- BPB Publications
-
- 2021-04-19
- 172 pages
- Ebook
- Springer Nature
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