Logical and Relational Learning (Hardcover)

Luc De Raedt

ISBN 10: 3540200401 / ISBN 13: 9783540200406

Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2008

Riguardo questo articolo

Hardcover. This book constitutes the first textbook on inductive logic programming (ILP) and multi-relational data mining (MRDM). These subfields of data mining and machine learning are concerned with analyzing structured data that arise in numerous applications, such as bioinformatics, web mining, natural language processing, etc. The book explains some important techniques in detail by using case studies centered around well-known ILP or MRDM systems. These case studies are some of the "classics" of the field and also provide an adequate starting point for a more general discussion. Related systems and techniques are covered in a bibliographical section at the end of each chapter.The book addresses graduate students in computer science, data bases and artificial intelligence as well as practitioners of data mining and machine learning. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9783540200406

Dati bibliografici

• Titolo: Logical and Relational Learning (Hardcover)
• Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin
• Data di pubblicazione: 2008
• Lingua: Inglese
• ISBN 10: 3540200401
• ISBN 13: 9783540200406
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.

Dalla quarta di copertina

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.