Franklin

Foundations of Rule Learning [electronic resource] / by Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač.

Author/Creator:
Fürnkranz, Johannes https://orcid.org/0000-0002-1207-0159 author., Author,
Publication:
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Format/Description:
Book
1 online resource (XVIII, 334 pages)
Edition:
1st ed. 2012.
Series:
Computer Science (Springer-11645)
Cognitive technologies 1611-2482
Cognitive Technologies, 1611-2482
Contained In:
Springer eBooks
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Subjects:
Data mining.
Artificial intelligence.
Pattern perception.
Computers.
Statistics.
Local subjects:
Data Mining and Knowledge Discovery. (search)
Artificial Intelligence. (search)
Pattern Recognition. (search)
Computation by Abstract Devices. (search)
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. (search)
System Details:
text file PDF
Summary:
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.
Contents:
Part I. Introduction to Rule Learning
Machine Learning and Data Mining
Propositional Rule Learning
Relational Rule Learning
Part II. Elements of Rule Learning
Formal Framework for Rule Analysis
Features
Heuristics
Pruning of Rules and Rule Sets
Survey of Classification Rule Learning Systems Through the Analysis of Rule Learning Elements Used
Part III. Selected Topics in Predictive Induction
Part IV Selected Techniques and Applications.
Contributor:
Gamberger, Dragan. author., Author,
Lavrač, Nada. author., Author,
SpringerLink (Online service)
Other format:
Printed edition:
Printed edition:
Printed edition:
ISBN:
978-3-540-75197-7
9783540751977
Publisher Number:
10.1007/978-3-540-75197-7 doi
Access Restriction:
Restricted for use by site license.