Handbook of Relational Learning
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With increased interest in relational learning and the growing importance of machine learning, artificial intelligence, and data mining, inductive logic programming (ILP)—at the boundary between machine learning and logic programming—is on the rise. Authored by a leading researcher in the field, this timely book provides the first comprehensive introduction to be published in over ten years. It uses an accessible approach to present key concepts in ILP and provide an overview of possible applications. The book covers important topics in the field, including probability and statistics, statistical relational learning, experimental design, and combinatorial algorithms.
