Machine Learning : Discriminative and Generative /
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...
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| Format: | eBook |
| Language: | English |
| Published: |
Boston, MA :
Springer US,
2004.
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| Series: | International series in engineering and computer science ;
755. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (xvii, 200 pages) |
| ISBN: | 9781441990112 (electronic bk.) 1441990119 (electronic bk.) |
| ISSN: | 0893-3405 ; |