Jebara Machine Learning

Discriminative and Generative
Erscheinungsjahr 2012
ISBN: 978-1-4419-9011-2
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 755, 200 Seiten, eBook

Reihe: The Springer International Series in Engineering and Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Jebara, Tony

Inhaltsverzeichnis

1. Introduction.- 2. Generative Versus Discriminative Learning.- 3. Maximum Entropy Discrimination.- 4. Extensions to Med.- 5. Latent Discrimination.- 6. Conclusion.- 7. Appendix.