Logical and Relational Learning

Inbunden, Engelska, 2008

AvLuc De Raedt

709 kr

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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.

Produktinformation

Utgivningsdatum2008-09-12
Mått155 x 235 x 28 mm
Vikt770 g
FormatInbunden
SpråkEngelska
SerieCognitive Technologies
Antal sidor387
Upplaga2008
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540200406

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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)