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 and of 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 the artificial intelligence and other computer science communities. Open research issues, suggesting promising directions for future research, are also discussed.