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Book synopsis

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 alarger 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.

Book details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Machine Learning
• Subtitle: Discriminative and Generative
• Author: Tony Jebara
• Language: English
• Binding: Hardback
• Number of pages: 200
• EAN: 9781402076473
• ISBN: 1402076479
• ID: 01418105
• Publisher: Springer-Verlag New York Inc.
• Weight: 505 g
• Dimensions: 235 × 155 × 18 mm
• Date of publishing: 31. December 2003

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