logical relational learning (33 resultados)

Paperback. Condición: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condición: New.

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Hardcover. Condición: new. Hardcover. This book constitutes the first textbook on inductive logic programming (ILP) and multi-relational data mining (MRDM). These subfields of data mining and machine learning are concerned with analyzing structured data that arise in numerous applications, such as bioinformatics, web mining, natural language processing, etc. The book explains some important techniques in detail by using case studies centered around well-known ILP or MRDM systems. These case studies are some of the "classics" of the field and also provide an adequate starting point for a more general discussion. Related systems and techniques are covered in a bibliographical section at the end of each chapter.The book addresses graduate students in computer science, data bases and artificial intelligence as well as practitioners of data mining and machine learning. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condición: New.

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Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

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Condición: New. pp. 404.

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Condición: As New. Unread book in perfect condition.

Paperback. Condición: Brand New. 388 pages. 9.00x6.00x0.91 inches. In Stock.

Hardcover. Condición: Brand New. 1st edition. 388 pages. German language. 9.45x6.38x1.02 inches. In Stock.

Taschenbuch. Condición: 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.

Buch. Condición: 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.

hardcover. Condición: New. In shrink wrap. Looks like an interesting title!

Taschenbuch. Condición: 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.

Buch. Condición: 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.

Condición: Hervorragend. Zustand: Hervorragend | Seiten: 404 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Condición: Sehr gut. Zustand: Sehr gut | Seiten: 404 | Sprache: Englisch | Produktart: Bücher | 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.

Paperback. Condición: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condición: new. Hardcover. This book constitutes the first textbook on inductive logic programming (ILP) and multi-relational data mining (MRDM). These subfields of data mining and machine learning are concerned with analyzing structured data that arise in numerous applications, such as bioinformatics, web mining, natural language processing, etc. The book explains some important techniques in detail by using case studies centered around well-known ILP or MRDM systems. These case studies are some of the "classics" of the field and also provide an adequate starting point for a more general discussion. Related systems and techniques are covered in a bibliographical section at the end of each chapter.The book addresses graduate students in computer science, data bases and artificial intelligence as well as practitioners of data mining and machine learning. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condición: 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.

Buch. Condición: 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.

Condición: New. Print on Demand pp. 404 77 Illus.

Condición: New. PRINT ON DEMAND pp. 404.

Condición: 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&nbspThis first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and e.