TY  - BOOK
AU  - Raedt,Luc De
ED  - SpringerLink (Online service)
TI  - Logical and Relational Learning
T2  - Cognitive Technologies,
SN  - 9783540688563
AV  - QA76.758 
U1  - 005.1 23
PY  - 2008///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Software engineering
KW  - Database management
KW  - Data mining
KW  - Information storage and retrieval
KW  - Artificial intelligence
KW  - Computer Science
KW  - Software Engineering/Programming and Operating Systems
KW  - Artificial Intelligence (incl. Robotics)
KW  - Data Mining and Knowledge Discovery
KW  - Database Management
KW  - Information Storage and Retrieval
KW  - Information Systems Applications (incl. Internet)
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-540-68856-3
ER  -