Phase Transitions in Machine Learning

Inbunden, Engelska, 2011

1 319 kr

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Beskrivning

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.

Produktinformation

Utgivningsdatum:2011-06-16
Mått:195 x 254 x 27 mm
Vikt:1 100 g
Format:Inbunden
Språk:Engelska
Antal sidor:410
Förlag:Cambridge University Press
ISBN:9780521763912

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Mer om författaren

Antoine Cornuéjols is Full Professor of Computer Science at the AgroParisTech Engineering School in Paris. Attilio Giordana is Full Professor of Computer Science at the University of Piemonte Orientale in Italy. Lorenza Saitta is a Full Professor of Computer Science at the University of Piemonte Orientale in Italy.

Recensioner i media

"... it is still an open question whether this will be one of the basic tools for understanding machine learning problems and methods in the future. Naturally, this book is an essential source for researchers who want to find answers to these questions." Joe Hernandez-Orallo, Computing Reviews

Innehållsförteckning

Preface; Acknowledgements; Notation; 1. Introduction; 2. Statistical physics and phase transitions; 3. The satisfiability problem; 4. Constraint satisfaction problems; 5. Machine learning; 6. Searching the hypothesis space; 7. Statistical physics and machine learning; 8. Learning, SAT, and CSP; 9. Phase transition in FOL covering test; 10. Phase transitions and relational learning; 11. Phase transitions in grammatical inference; 12. Phase transitions in complex systems; 13. Phase transitions in natural systems; 14. Discussions and open issues; Appendix A. Phase transitions detected in two real cases; Appendix B. An intriguing idea; References; Index.