Machine Learning

Discriminative and Generative

AvTony Jebara

Häftad, Engelska, 2012

Del 755 i serien Springer International Series in Engineering and Computer Science

1 105 kr

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1 138 kr

E-bok

1 413 kr

Beskrivning

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.

Produktinformation

Utgivningsdatum:2012-09-27
Mått:155 x 235 x 13 mm
Vikt:347 g
Format:Häftad
Språk:Engelska
Serie:Springer International Series in Engineering and Computer Science
Antal sidor:200
Förlag:Springer-Verlag New York Inc.
ISBN:9781461347569

Utforska kategorier

Matematisk statistik inom Naturvetenskap och teknik
Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Recensioner i media

From the reviews: "This book aims to unite two powerful approaches in machine learning: generative and discriminative. ... Researchers from the generative or discriminative schools will find this book a nice bridge to the other paradigm." (C. Andy Tsao, Mathematical Reviews, Issue 2005 k)

Innehållsförteckning

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

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