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English
Cambridge University Press
16 June 2011
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.

By:   , ,
Imprint:   Cambridge University Press
Country of Publication:   United Kingdom
Dimensions:   Height: 254mm,  Width: 195mm,  Spine: 27mm
Weight:   1.100kg
ISBN:   9780521763912
ISBN 10:   0521763916
Pages:   410
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

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.

Reviews for Phase Transitions in Machine Learning

. .. it is still an open question whether this will be one of the basic tools for understanding machine learning problems and methods in the future. Naturally, this book is an essential source for researchers who want to find answers to these questions. Joe Hernandez-Orallo, Computing Reviews


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