Phase Transitions In Machine Learning
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Phase Transitions in Machine Learning
Author: Lorenza Saitta
language: en
Publisher: Cambridge University Press
Release Date: 2011-06-16
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.
Phase Transitions in Machine Learning
This state-of-the-art overview of the field describes how phase transitions occur and teaches appropriate methods for tackling the consequent problems.
Phase Transitions in Machine Learning
This state-of-the-art overview of the field describes how phase transitions occur and teaches appropriate methods for tackling the consequent problems.