02703nam a22003378i 4500001001600000003000700016008004100023020001800064020001800082035002000100041000800120082001600128100002400144245011000168264007100278300003800349336002600387337002600413338003600439500001300475505068400488520086101172650002202033650004802055700003102103700003302134856005502167932003202222596000602254949010502260CR9780511975509UkCbUP101011s2011||||enk o ||1 0|eng|d a9780511975509 a9780521763912 a(Sirsi) a792880 aeng00a006.3/12231 aSaitta, L.,eauthor10aPhase transitions in machine learningh[E-Book] /cLorenza Saitta, Attilio Giordana, Antoine CornuĂ©jols. 1aCambridge :bCambridge University Press,c2011e(CUP)fCUP20200108 a1 online resource (xv, 383 pages) atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier aenglisch8 aMachine 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. aPhase 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. 0aMachine learning. 0aPhase transformations (Statistical physics)1 aGiordana, Attilio,eauthor1 aCornuejols, Antoine,eauthor40uhttps://doi.org/10.1017/CBO9780511975509zVolltext aCambridgeCore (Order 30059) a1 aXX(792880.1)wAUTOc1i792880-1001lELECTRONICmZBrNsYtE-BOOKu8/1/2020xUNKNOWNzUNKNOWN1ONLINE