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:
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| Weitere beteiligte Personen: | , |
| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2011
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| Links: | 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. |
| Umfang: | 1 Online-Ressource (xv, 383 Seiten) |
| ISBN: | 9780511975509 |
Internformat
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| spelling | Saitta, L. 1944- Phase transitions in machine learning Lorenza Saitta, Attilio Giordana, Antoine Cornuéjols Cambridge Cambridge University Press 2011 1 Online-Ressource (xv, 383 Seiten) txt c cr 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. Cornuejols, Antoine Giordana, Attilio Erscheint auch als Druck-Ausgabe 9780521763912 |
| spellingShingle | Saitta, L. 1944- Phase transitions in machine learning |
| title | Phase transitions in machine learning |
| title_auth | Phase transitions in machine learning |
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| 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 |
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