Foundations of Rule Learning

Foundations of Rule Learning

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Rules a€“ the clearest, most explored and best understood form of knowledge representation a€“ are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.Johannes FA¼rnkranz, Dragan Gamberger, Nada LavraA ... There may be many reasons for noise in databases: manual data collection and data entry are error- prone, ... some attribute values may not always be available, judgments obtained from human experts are often inconsistent, ... On the other hand, in a noise-free dataset, once the underlying theory has been found, additional training examples anbsp;...

Title:Foundations of Rule Learning
Author: Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
Publisher:Springer Science & Business Media - 2012-11-06

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