Luc De Raedt

Logical and Relational Learning

• Gebundenes Buch

Produktbeschreibung

Iusethetermlogicalandrelationallearning torefertothesub?eldofarti?cial intelligence,machinelearninganddataminingthatisconcernedwithlearning in expressive logical or relational representations. It is the union of inductive logic programming, (statistical) relational learning and multi-relational data mining, which all have contributed techniques for learning from data in re- tional form. Even though some early contributions to logical and relational learning are about forty years old now, it was only with the advent of - ductive logic programming in the early 1990s that the ?eld became popular. Whereas initial work was often concerned with logical (or logic programming) issues,thefocushasrapidlychangedtothediscoveryofnewandinterpretable knowledge from structured data, often in the form of rules, and soon imp- tant successes in applications in domains such as bio- and chemo-informatics and computational linguistics were realized. Today, the challenges and opp- tunities of dealing with structured data and knowledge have been taken up by the arti?cial intelligence community at large and form the motivation for a lot of ongoing research. Indeed, graph, network and multi-relational data mining are now popular themes in data mining, and statistical relational learning is receiving a lot of attention in the machine learning and uncertainty in art- cial intelligence communities. In addition, the range of tasks for which logical and relational techniques have been developed now covers almost all machine learning and data mining tasks.

Produktdetails

• Cognitive Technologies
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 10958292, 978-3-540-20040-6
• 2008
• Seitenzahl: 404
• Erscheinungstermin: 12. September 2008
• Englisch
• Abmessung: 241mm x 160mm x 28mm
• Gewicht: 710g
• ISBN-13: 9783540200406
• ISBN-10: 3540200401
• Artikelnr.: 14811907

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)