Luc De Raedt

Logical and Relational Learning (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 387
• Erscheinungstermin: 27. September 2008
• Englisch
• ISBN-13: 9783540688563
• Artikelnr.: 44134183

Autorenporträt

Luc De Raedt is currently a full professor (C4) of computer science at the Albert-Ludwigs-University Freiburg and head of the Machine Learning lab. Before coming to Freiburg in 1999, he held positions as (parttime) senior lecturer, lecturer and assistant at the Department of Computer Science of the Katholieke Universiteit Leuven (Belgium) and as post-doc of the Fund for Scientific Research, Flanders. He obtained his undergraduate degree as well as his Ph.D. in computer science from the Katholieke Universiteit Leuven (Belgium) in 1986 and 1991. His Ph.D. thesis was subsequently published by Academic Press. De Raedt has a rich experience in European Union research projects.'He (co-)coordinated the successful ESPRIT III and IV Inductive Logic Programming (1 and 2) projects, coordinated the IST assessment project APrIL, and the Marie Curie Training Site DAISY (Foundations of Intelligent Systems). He is at present also involved in the European IST-FET project cInQ belonging to FP5. De Raedt has (co)-organised several international workshops and conferences.

Inhaltsangabe

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.

Rezensionen

From the reviews:

"This book is an invaluable resource for anyone interested in exploiting the power of logical representations to learn from highly structured data. The book offers a systematic and innovative view of this important and rapidly developing area, combining technical depth and breadth of coverage. In Bristol, we use De Raedt's book as textbook for MSc students and as a reference for PhD students and researchers." (Peter A. Flach, University of Bristol)

"This book provides comprehensive coverage of logical and relational learning, with an overview of inductive logic programming, multi-relational data mining, and statistical relational learning. ... The book is replete with examples, exercises, and case studies. The case studies use popular logical and relational systems and applications. The ample use of illustrations, tables, and bullet lists makes the book more readable and understandable. ... very useful to students, researchers, and practitioners in the fields of machine learning, automated knowledge discovery, data mining, and related fields." (Alexis Leon, ACM Computing Reviews, July, 2009)