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Logical and Relational Learning [electronic resource] /edited by Luc De Raedt.

by Raedt, Luc De [editor.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Cognitive Technologies: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: XVI, 388p. 77 illus. online resource.ISBN: 9783540688563.Subject(s): Computer science | Database management | Data mining | Information storage and retrieval systems | Information systems | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Data Mining and Knowledge Discovery | Database Management | Information Storage and Retrieval | Information Systems Applications (incl.Internet)DDC classification: 006.3 Online resources: Click here to access online
Contents:
An Introduction to Logic -- An Introduction to Learning and Search -- Representations for Mining and Learning -- Generality and Logical Entailment -- The Upgrading Story -- Inducing Theories -- Probabilistic Logic Learning -- Kernels and Distances for Structured Data -- Computational Aspects of Logical and Relational Learning -- Lessons Learned.
In: Springer eBooksSummary: This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic. The author introduces the machine learning and representational foundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known logical and relational systems. The book is suitable for use in graduate courses and should be of interest to graduate students and researchers in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning. It contains numerous figures and exercises, and slides are available for many chapters.
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An Introduction to Logic -- An Introduction to Learning and Search -- Representations for Mining and Learning -- Generality and Logical Entailment -- The Upgrading Story -- Inducing Theories -- Probabilistic Logic Learning -- Kernels and Distances for Structured Data -- Computational Aspects of Logical and Relational Learning -- Lessons Learned.

This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic. The author introduces the machine learning and representational foundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known logical and relational systems. The book is suitable for use in graduate courses and should be of interest to graduate students and researchers in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning. It contains numerous figures and exercises, and slides are available for many chapters.

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