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Machine Learning

Discriminative and Generative

Springer US,
149,79 € Lieferbar in 5-7 Tagen
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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.


Titel: Machine Learning
Autoren/Herausgeber: Tony Jebara
Aus der Reihe: The Springer International Series in Engineering and Computer Science
Ausgabe: 2004

ISBN/EAN: 9781402076473

Seitenzahl: 200
Format: 23,5 x 15,5 cm
Produktform: Hardcover/Gebunden
Gewicht: 1,090 g
Sprache: Englisch - Newsletter
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