Phase transitions in machine learning:
Phase 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 det...
Gespeichert in:
| Beteilige Person: | |
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2011
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| Schlagwörter: | |
| Links: | https://doi.org/10.1017/CBO9780511975509 https://doi.org/10.1017/CBO9780511975509 https://doi.org/10.1017/CBO9780511975509 |
| Zusammenfassung: | Phase 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 |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Umfang: | 1 online resource (xv, 383 pages) |
| ISBN: | 9780511975509 |
| DOI: | 10.1017/CBO9780511975509 |
Internformat
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| 505 | 8 | |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 | |
| 520 | |a Phase 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 | 4 | |a Machine learning | |
| 650 | 4 | |a Phase transformations (Statistical physics) | |
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| 700 | 1 | |a Giordana, Attilio |e Sonstige |4 oth | |
| 700 | 1 | |a Cornuejols, Antoine |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druckausgabe |z 978-0-521-76391-2 |
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Datensatz im Suchindex
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| author | Saitta, L. 1944- |
| author_facet | Saitta, L. 1944- |
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| contents | 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 |
| ctrlnum | (ZDB-20-CBO)CR9780511975509 (OCoLC)838983888 (DE-599)BVBBV043943453 |
| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1017/CBO9780511975509 |
| format | Electronic eBook |
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| indexdate | 2024-12-20T17:49:21Z |
| institution | BVB |
| isbn | 9780511975509 |
| language | English |
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| spelling | Saitta, L. 1944- Verfasser aut Phase transitions in machine learning Lorenza Saitta, Attilio Giordana, Antoine Cornuéjols Cambridge Cambridge University Press 2011 1 online resource (xv, 383 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) 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 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 Machine learning Phase transformations (Statistical physics) Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Phasenumwandlung (DE-588)4132140-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Phasenumwandlung (DE-588)4132140-6 s 1\p DE-604 Giordana, Attilio Sonstige oth Cornuejols, Antoine Sonstige oth Erscheint auch als Druckausgabe 978-0-521-76391-2 https://doi.org/10.1017/CBO9780511975509 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Saitta, L. 1944- 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. 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 learning Phase transformations (Statistical physics) Maschinelles Lernen (DE-588)4193754-5 gnd Phasenumwandlung (DE-588)4132140-6 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4132140-6 |
| title | Phase transitions in machine learning |
| title_auth | Phase transitions in machine learning |
| title_exact_search | Phase transitions in machine learning |
| title_full | Phase transitions in machine learning Lorenza Saitta, Attilio Giordana, Antoine Cornuéjols |
| title_fullStr | Phase transitions in machine learning Lorenza Saitta, Attilio Giordana, Antoine Cornuéjols |
| title_full_unstemmed | Phase transitions in machine learning Lorenza Saitta, Attilio Giordana, Antoine Cornuéjols |
| title_short | Phase transitions in machine learning |
| title_sort | phase transitions in machine learning |
| topic | Machine learning Phase transformations (Statistical physics) Maschinelles Lernen (DE-588)4193754-5 gnd Phasenumwandlung (DE-588)4132140-6 gnd |
| topic_facet | Machine learning Phase transformations (Statistical physics) Maschinelles Lernen Phasenumwandlung |
| url | https://doi.org/10.1017/CBO9780511975509 |
| work_keys_str_mv | AT saittal phasetransitionsinmachinelearning AT giordanaattilio phasetransitionsinmachinelearning AT cornuejolsantoine phasetransitionsinmachinelearning |