Machine Learning: Discriminative and Generative

di Tony Jebara (Autore)

Springer-Verlag New York Inc., 2003

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.

• Autore: Tony Jebara
• Editore: Springer-Verlag New York Inc.
• Collana: The Springer International Series in Engineering and Computer Science
• Anno: 2003
• Rilegatura: Hardback
• Pagine: 200 p.
• Dimensioni: 235 x 155 mm
• EAN: 9781402076473

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