Logical and Relational Learning - Couverture souple

De Raedt, Luc

Couverture souple

ISBN 10 : 3642057489 ISBN 13 : 9783642057489

Editeur : Springer, 2010

Synopsis

<p>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. </p>

A propos du livre

À propos de l'auteur

Luc De Raedt is a full professor of computer science at the KU Leuven (Belgium), where he is director of the Lab for Declarative Languages and Artificial Intelligence, and where he also obtained his Ph.D. He is also a former professor of computer science of the Albert-Ludwigs-University Freiburg (Germany) and chair of its lab for Natural Language Processsing and Machine Learning. Luc De Raedt's research interests are in artificial intelligence, machine learning, and data mining, as well as their applications. He is currently working on probabilistic logic learning (sometimes called statistical relational learning), which combines probabilistic reasoning methods with logical representations and machine learning, the integration of constraint programming with data mining and machine learning principles, the development of programming languages for machine learning, and analyzing graph and network data. He is also interested in applications of these methods to chemo- and bio-informatics,to natural language processing, vision, robotics, and action and activity learning. He was program (co)-chair of the 7th ECML Machine Learning (1994, Catania, Sicily), the 5th ILP (1995, Leuven, Belgium), the first ECMLPKDD (2001, Freiburg, Germany), the 22nd ICML Learning (2005, Bonn, Germany) and the 20th ECAI (2012, Montpellier, France). He is an area/action editor of TPLP, JMLR, MLJ, AIJ, and formerly of JAIR. He is also a member of the editorial boards of NGC, AI Communications, Informatica, DMKD, and the Journal of Applied Logic. He was an elected and founding member of the board of the International Machine Learning Society from 2004-2011. In 2005, he was elected as an ECCAI fellow and four of his students have won the ECCAI dissertation award for the best European dissertation in AI.

Luc De Raedt is a full professor of computer science at the KU Leuven (Belgium), where he is director of the Lab for Declarative Languages and Artificial Intelligence, and where he also obtained his Ph.D. He is also a former professor of computer science of the Albert-Ludwigs-University Freiburg (Germany) and chair of its lab for Natural Language Processsing and Machine Learning. Luc De Raedt's research interests are in artificial intelligence, machine learning, and data mining, as well as their applications. He is currently working on probabilistic logic learning (sometimes called statistical relational learning), which combines probabilistic reasoning methods with logical representations and machine learning, the integration of constraint programming with data mining and machine learning principles, the development of programming languages for machine learning, and analyzing graph and network data. He is also interested in applications of these methods to chemo- and bio-informatics, to natural language processing, vision, robotics, and action and activity learning. He was program (co)-chair of the 7th ECML Machine Learning (1994, Catania, Sicily), the 5th ILP (1995, Leuven, Belgium), the first ECMLPKDD (2001, Freiburg, Germany), the 22nd ICML Learning (2005, Bonn, Germany) and the 20th ECAI (2012, Montpellier, France). He is an area/action editor of TPLP, JMLR, MLJ, AIJ, and formerly of JAIR. He is also a member of the editorial boards of NGC, AI Communications, Informatica, DMKD, and the Journal of Applied Logic. He was an elected and founding member of the board of the International Machine Learning Society from 2004-2011. In 2005, he was elected as an ECCAI fellow and four of his students have won the ECCAI dissertation award for the best European dissertation in AI.

Sriraam Natarajan is an assistant professor at Indiana University. He was previously an assistant professor at Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison, and graduated with his Ph.D. from Oregon State University. His research interests lie in the field of artificial intelligence, with emphasis on machine learning, stati

À propos de la quatrième de couverture

<p>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.</p><p>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. </p><p>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.</p>

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2010
• Langue: anglais
• ISBN 10: 3642057489
• ISBN 13: 9783642057489
• Reliure: Broché
• Nombre de pages: 402
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne