Title Statements
- Full title :Phase transitions in machine learning / Lorenza Saitta, Attilio Giordana, Antoine Cornuéjols.
Summary
"Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning and 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"--Résumé de l'éditeur.
Read More ↓Edition Statements
- Publication Date:2011.
Publication Statements
- Publisher(s):Cambridge University Press
- Place of Publication:Cambridge
- Language:eng
- Publication Date:2011.
The Physical Object
- Description:xv, 383 p. : : ill. ; : 26 cm
Edition Identifiers
- ISBN:9780521763912, 0521763916
- oclc:ocn794714628
- LC Classification:Q324.4
- Dewey Decimal:006.3/1
Table of Contents
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.
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Phase transitions in machine learning -- (.pdf)
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Phase Transitions in Machine Learning -- (.pdf)
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