Coverart for item
The Resource Foundations of rule learning, Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač, (electronic resource)

Foundations of rule learning, Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač, (electronic resource)

Label
Foundations of rule learning
Title
Foundations of rule learning
Statement of responsibility
Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
Creator
Contributor
Subject
Language
eng
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
Member of
http://library.link/vocab/creatorName
Fürnkranz, Johannes
Dewey number
006.3/1
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Gamberger, Dragan
  • Lavrač, Nada
Series statement
Cognitive technologies
http://library.link/vocab/subjectName
  • Machine learning
  • Data mining
Label
Foundations of rule learning, Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač, (electronic resource)
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Contents
  • Pruning of Rules and Rule Sets
  • Beyond Concept Learning
  • Supervised Descriptive Rule Learning
  • Selected Applications
  • Machine Learning and Data Mining
  • Rule Learning in a Nutshell
  • Formal Framework for Rule Analysis
  • Features
  • Relational Features
  • Learning Single Rules
  • Rule Evaluation Measures
  • Learning Rule Sets
Control code
ocn821217379
Dimensions
unknown
Extent
1 online resource
Form of item
online
Isbn
9783540751977
Isbn Type
(electronic bk.)
Other physical details
ill
Specific material designation
remote
System control number
(OCoLC)821217379
Label
Foundations of rule learning, Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač, (electronic resource)
Publication
Bibliography note
Includes bibliographical references and index
Contents
  • Pruning of Rules and Rule Sets
  • Beyond Concept Learning
  • Supervised Descriptive Rule Learning
  • Selected Applications
  • Machine Learning and Data Mining
  • Rule Learning in a Nutshell
  • Formal Framework for Rule Analysis
  • Features
  • Relational Features
  • Learning Single Rules
  • Rule Evaluation Measures
  • Learning Rule Sets
Control code
ocn821217379
Dimensions
unknown
Extent
1 online resource
Form of item
online
Isbn
9783540751977
Isbn Type
(electronic bk.)
Other physical details
ill
Specific material designation
remote
System control number
(OCoLC)821217379

Library Locations

    • InternetBorrow it
      Albany, Auckland, 0632, NZ
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