Phase Transitions in Machine Learning

ISBN-13: 9780521763912 / Angielski / Twarda / 2011 / 410 str.

Lorenza Saitta

Opis

This state-of-the-art overview of the field describes how phase transitions occur and teaches appropriate methods for tackling the consequent problems.

Recenzje

"... 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

Spis treści

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.