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Machine Learning

Discriminative and Generative

Medium: Buch
ISBN: 978-1-4613-4756-9
Verlag: Springer US
Erscheinungstermin: 27.09.2012
Lieferfrist: bis zu 10 Tage

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.


Produkteigenschaften


  • Artikelnummer: 9781461347569
  • Medium: Buch
  • ISBN: 978-1-4613-4756-9
  • Verlag: Springer US
  • Erscheinungstermin: 27.09.2012
  • Sprache(n): Englisch
  • Auflage: Softcover Nachdruck of the original 1. Auflage 2004
  • Serie: The Springer International Series in Engineering and Computer Science
  • Produktform: Kartoniert
  • Gewicht: 347 g
  • Seiten: 200
  • Format (B x H x T): 155 x 235 x 13 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

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