Phase Transitions in Machine Learning - Hardcover

Saitta, Lorenza; Giordana, Attilio; Cornuéjols, Antoine

Hardcover

ISBN 10: 0521763916 ISBN 13: 9780521763912

Publisher: Cambridge University Press, 2011

Synopsis

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.

About this title

Review:

"... 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

Book Description:

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.

About this edition

• Publisher: Cambridge University Press
• Publication date: 2011
• ISBN 10: 0521763916
• ISBN 13: 9780521763912
• Binding: Hardcover
• Number of pages: 410