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Logical Relational Learning by Raedt Luc (29 results)
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Hardback or Cased Book. Condition: New. Logical and Relational Learning 1.8. Book.
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Logical and Relational Learning (Cognitive Technologies)
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Logical And Relational Learning
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Condition: New. pp. 404.
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Logical and Relational Learning (Cognitive Technologies)
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Logical and Relational Learning
Published by Springer Berlin Heidelberg, 2008
ISBN 10: 3642057489 ISBN 13: 9783642057489
Language: English
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Add to basketPaperback. Condition: Brand New. 388 pages. 9.00x6.00x0.91 inches. In Stock.
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Logical And Relational Learning: From Ilp to Mrdm
Published by Springer-Verlag New York Inc, 2008
ISBN 10: 3540200401 ISBN 13: 9783540200406
Language: English
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Add to basketHardcover. Condition: Brand New. 1st edition. 388 pages. German language. 9.45x6.38x1.02 inches. In Stock.
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Logical and Relational Learning
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Sep 2008, 2008
ISBN 10: 3540200401 ISBN 13: 9783540200406
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Add to basketBuch. Condition: Neu. 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.
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Logical and Relational Learning
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2010, 2010
ISBN 10: 3642057489 ISBN 13: 9783642057489
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Add to basketTaschenbuch. Condition: Neu. 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.
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Logical and Relational Learning
Published by Springer Berlin Heidelberg, 2008
ISBN 10: 3540200401 ISBN 13: 9783540200406
Language: English
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hardcover. Condition: New. In shrink wrap. Looks like an interesting title!
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Logical and Relational Learning
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642057489 ISBN 13: 9783642057489
Language: English
US$ 64.64
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Add to basketTaschenbuch. Condition: Neu. 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.
-
Logical and Relational Learning
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2008
ISBN 10: 3540200401 ISBN 13: 9783540200406
Language: English
US$ 64.64
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Add to basketBuch. Condition: Neu. 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.
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Logical And Relational Learning
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Logical and Relational Learning
Published by Springer Berlin Heidelberg Sep 2008, 2008
ISBN 10: 3540200401 ISBN 13: 9783540200406
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 404 pp. Englisch.
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Logical and Relational Learning
Published by Springer Berlin Heidelberg Feb 2010, 2010
ISBN 10: 3642057489 ISBN 13: 9783642057489
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
US$ 64.64
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 404 pp. Englisch.
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Add to basketCondition: New. Print on Demand pp. 404 77 Illus.
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Logical and Relational Learning
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642057489 ISBN 13: 9783642057489
Language: English
US$ 57.08
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. First textbook on multirelational data mining and inductive logic programming This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and e.
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Logical and Relational Learning
Published by Springer Berlin Heidelberg, 2008
ISBN 10: 3540200401 ISBN 13: 9783540200406
Language: English
US$ 57.08
Convert currencyUS$ 57.48 shipping from Germany to U.S.A.Quantity: Over 20 available
Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. First textbook on multirelational data mining and inductive logic programming This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and e.
