Logical and Relational Learning

Luc De Raedt

ISBN 10: 3642057489 / ISBN 13: 9783642057489

Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2010, 2010

A propos de cet article

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 404 pp. Englisch.

N° de réf. du vendeur 9783642057489

Détails bibliographiques

• Titre: Logical and Relational Learning
• Éditeur: Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2010
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 3642057489
• ISBN à 13 chiffres: 9783642057489
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 22 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 22 millimètres de profondeur
• Poids: 610 grammes

A propos de ce titre

Synopsis

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.

Présentation de l'éditeur

The first textbook ever to cover multi-relational data mining and inductive logic programming, this book fully explores logical and relational learning. Ideal for graduate students and researchers, it also looks at statistical relational learning.