Phase transitions in machine learning

Machine generated contents note: Preface; Acknowledgements; 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. Learn...

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1. Verfasser: Saitta, Lorenza (VerfasserIn)
Weitere Verfasser: Giordana, Attilio (VerfasserIn), Cornuéjols, Antoine (VerfasserIn)
Format: UnknownFormat
Sprache:eng
Veröffentlicht: Cambridge u.a. Cambridge University Press 2011
Ausgabe:1st publ.
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Zusammenfassung:Machine generated contents note: Preface; Acknowledgements; 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. Relationships with 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.
Machine generated contents note: Preface; Acknowledgements; 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. Relationships with 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
"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"--
Beschreibung:Includes bibliographical references and index
Beschreibung:XV, 383 S.
graph. Darst.
25 cm
ISBN:0521763916
0-521-76391-6
9780521763912
978-0-521-76391-2