000			02798nam a22003618i 4500
001			CR9780511975509
003			UkCbUP
005			20200124160255.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			101011s2011||||enk o ||1 0|eng|d
020			_a9780511975509 (ebook)
020			_z9780521763912 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQ324.4 _b.S25 2011
082	0	0	_a006.3/1 _223
100	1		_aSaitta, L. _q(Lorenza), _d1944- _eauthor.
245	1	0	_aPhase transitions in machine learning / _cLorenza Saitta, Attilio Giordana, Antoine Cornuéjols.
264		1	_aCambridge : _bCambridge University Press, _c2011.
300			_a1 online resource (xv, 383 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	8		_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.
520			_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.
650		0	_aMachine learning.
650		0	_aPhase transformations (Statistical physics)
700	1		_aGiordana, Attilio, _eauthor.
700	1		_aCornuejols, Antoine, _eauthor.
776	0	8	_iPrint version: _z9780521763912
856	4	0	_uhttps://doi.org/10.1017/CBO9780511975509
999			_c519857 _d519855