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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.

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



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.