Phase Transitions in Machine Learning
Phase Transitions in Machine Learning
Attilio Giordana , Università degli Studi del Piemonte Orientale Amedeo Avogadro
Antoine Cornuéjols , AgroParis Tech (INA-PG)
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$120.00
USDPhase 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.
- Deep analysis of foundational issues in machine learning will interest a variety of readers from outside the field, such as cognitive scientists and philosophers
- Detailed explanations are aided by examples and applications
- Suitable textbook for graduate-level courses
Reviews & endorsements
"... 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
Product details
July 2011Hardback
9780521763912
410 pages
254 × 195 × 27 mm
1.1kg
90 b/w illus. 10 tables
Temporarily unavailable - available from TBC
Table of Contents
- 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.
