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" Machine Learning "
by Tony Jebara.
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BL
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614810
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dltt
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| Main Entry
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Jebara, Tony.
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| Title & Author
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Machine Learning : Discriminative and Generative /\ by Tony Jebara.
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| Publication Statement
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Boston, MA :: Springer US :: Imprint: Springer,, 2004.
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| Series Statement
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International Series in Engineering and Computer Science,; 755
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| ISBN
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9781441990112
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: 9781461347569
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| Contents
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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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| Abstract
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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.
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Computer science.
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Information storage and retrieval systems.
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Artificial intelligence.
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Computer vision.
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Statistics.
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SpringerLink (Online service)
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