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Phase transitions in machine learning

[NT 42944] Record Type:	[NT 1579] Language materials, printed : [NT 40817] monographic
[NT 47261] Author:	SaittaL., 1944-
[NT 47353] Alternative Intellectual Responsibility:	GiordanaAttilio,
[NT 47353] Alternative Intellectual Responsibility:	CornuejolsAntoine,
[NT 47351] Place of Publication:	Cambridge
[NT 47263] Published:	Cambridge University Press;
[NT 47352] Year of Publication:	2011
[NT 47264] Description:	xv, 383 p.ill. : 26 cm.;
[NT 47266] Subject:	Phase transformations (Statistical physics) -
[NT 47266] Subject:	Machine learning. -
[NT 51398] 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"--Provided by publisher
[NT 50961] ISBN:	978-0-521-76391-2bound
[NT 50961] ISBN:	0-521-76391-6bound
[NT 60779] Content Note:	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 in machine learning
Saitta, L.

Phase transitions in machine learning / Lorenza Saitta, Attilio Giordana, Antoine Cornuejols - Cambridge : Cambridge University Press, 2011. - xv, 383 p. ; ill. ; 26 cm..
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.
Includes bibliographical references (p. 355-374) and index..
ISBN 978-0-521-76391-2ISBN 0-521-76391-6
Phase transformations (Statistical physics)Machine learning.

Giordana, Attilio

Phase transitions in machine learning
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010 1 $a 0-521-76391-6 $b bound $d NT$2369
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101 0 $a eng
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200 1 $a Phase transitions in machine learning $f Lorenza Saitta, Attilio Giordana, Antoine Cornuejols
210 $a Cambridge $d 2011 $c Cambridge University Press
215 1 $a xv, 383 p. $c ill. $d 26 cm.
320 $a Includes bibliographical references (p. 355-374) and index.
327 0 $a 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
330 $a "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"--Provided by publisher
606 # $a Phase transformations (Statistical physics) $3 134831
606 # $a Machine learning. $3 126290
676 $a 006.31
700 1 $a Saitta $b L. $g Lorenza $f 1944- $3 271803
701 1 $a Giordana $b Attilio $3 271804
701 1 $a Cornuejols $b Antoine $3 271805
801 0 $a cw $b 嶺東科技大學圖書館 $g CCR