Machine Learning

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Beschreibung

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.



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)



Autorentext
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.


Klappentext

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.



Zusammenfassung

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.



Inhalt
- 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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Produktinformationen

Titel
Machine Learning
Untertitel
Discriminative and Generative
Autor
EAN
9781461347569
ISBN
1461347564
Format
Kartonierter Einband
Herausgeber
Springer US
Genre
Informatik
Anzahl Seiten
224
Gewicht
347g
Größe
H235mm x B155mm x T12mm
Jahr
2012
Untertitel
Englisch
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