Foundations of Rule Learning

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 machin...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Fürnkranz, Johannes (Autor), Gamberger, Dragan (Autor), Lavrač, Nada (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Edición:1st ed. 2012.
Colección:Cognitive Technologies,
Temas:
Data mining.
Artificial intelligence.
Pattern recognition systems.
Computer science.
Statistics .
Data Mining and Knowledge Discovery.
Artificial Intelligence.
Automated Pattern Recognition.
Theory of Computation.
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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