Logical and Relational Learning

Luc De Raedt

ISBN 10: 3540200401 / ISBN 13: 9783540200406

Edité par Springer, 2008

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9783540200406

Détails bibliographiques

• Titre: Logical and Relational Learning
• Éditeur: Springer
• Date de parution: 2008
• Langue: anglais
• ISBN à 10 chiffres: 3540200401
• ISBN à 13 chiffres: 9783540200406
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 28 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 28 millimètres de profondeur
• Poids: 770 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

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.