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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.
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Informazioni sugli autori
Antoine Cornuéjols is Full Professor of Computer Science at the AgroParisTech Engineering School in Paris.
Attilio Giordana is Full Professor of Computer Science at the University of Piemonte Orientale in Italy.
Lorenza Saitta is a Full Professor of Computer Science at the University of Piemonte Orientale in Italy.
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Getting Hooked: Rationality and Addiction
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Hardcover. Condizione: new. Hardcover. 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. This state-of-the-art overview describes how phase transitions occur and teaches appropriate methods for tackling the consequent problems. Weaving together fundamental aspects of computer science, statistical physics and machine learning, it provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9780521763912
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Phase Transitions in Machine Learning (Hardcover)
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Cambridge University Press, Cambridge, 2011
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ISBN 13: 9780521763912
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Hardcover. Condizione: new. Hardcover. 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. This state-of-the-art overview describes how phase transitions occur and teaches appropriate methods for tackling the consequent problems. Weaving together fundamental aspects of computer science, statistical physics and machine learning, it provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780521763912
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Phase Transitions in Machine Learning (Hardcover)
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Cambridge University Press, Cambridge, 2011
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ISBN 13: 9780521763912
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Hardcover. Condizione: new. Hardcover. 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. This state-of-the-art overview describes how phase transitions occur and teaches appropriate methods for tackling the consequent problems. Weaving together fundamental aspects of computer science, statistical physics and machine learning, it provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9780521763912
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