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Foundations of Rule Learning (Cognitive Technologies)
Johannes Fürnkranz; Dragan Gamberger; Nada Lavrač (Author)
·
Springer
· Hardcover
Foundations of Rule Learning (Cognitive Technologies) - Johannes Fürnkranz; Dragan Gamberger; Nada Lavrač
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Origin: U.S.A.
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Synopsis "Foundations of Rule Learning (Cognitive Technologies)"
Rules – the clearest, most explored and best understood form of knowledge representation – 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.
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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Hardcover.
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