Sinossi
Machine Learning is a powerful new field with many important practical applications. Thanks to the information age and flood of data, it has taken many domains by storm including biology, text processing, internet data organization, computer vision, speech recognition, computer-human interfaces, robotics, and artificial intelligence. This easy-access book covers the main contemporary themes and tools in machine learning, ranging from Bayesian probabilistic models to discriminative support-vector machines. Unlike previous books, it bridges these two schools of thought together within a common framework, elegantly connecting their various theories and combining their strengths into one common big-picture.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Recensione
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)
Contenuti
- List of Figures. List of Tables. - Preface. Acknowledgments. - 1. Introduction. - 2. Generative Versus Discriminative Learning. - 3. Maximum Entropy Discrimination. - 4. Extensions To MED. - 5. Latent Discrimination. - 6. Conclusion. - 7. Appendix. - Index.
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Machine Learning (eng)
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Machine Learning: Discriminative and Generative (The Springer International Series in Engineering and Computer Science)
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Machine Learning
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 224 pp. Englisch. Codice articolo 9781461347569
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 boo. Codice articolo 4193002
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Machine Learning | Discriminative and Generative
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Taschenbuch. Condizione: Neu. Machine Learning | Discriminative and Generative | Tony Jebara | Taschenbuch | xvii | Englisch | 2012 | Springer | EAN 9781461347569 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Codice articolo 105721152
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Machine Learning
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Machine Learning is a powerful new field with many important practical applications. Thanks to the information age and flood of data, it has taken many domains by storm including biology, text processing, internet data organization, computer vision, speech recognition, computer-human interfaces, robotics, and artificial intelligence. This easy-access book covers the main contemporary themes and tools in machine learning, ranging from Bayesian probabilistic models to discriminative support-vector machines. Unlike previous books, it bridges these two schools of thought together within a common framework, elegantly connecting their various theories and combining their strengths into one common big-picture.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 224 pp. Englisch. Codice articolo 9781461347569
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9781461347569
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