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Synopsis
What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual properties of different approaches.
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About the Author
M. E. Mueller is a Professor of Computer Science at the Bonn-Rhein-Sieg University of Applied Sciences.
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Relational Knowledge Discovery
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Cambridge University Press, 2012
ISBN 10: 0521190215
ISBN 13: 9780521190213
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Relational Knowledge Discovery
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Condition: New. Introductory textbook presenting relational methods in machine learning. Num Pages: 278 pages, 50 b/w illus. 100 exercises. BIC Classification: UYQ. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 253 x 173 x 19. Weight in Grams: 654. . 2012. Illustrated. hardcover. . . . . Seller Inventory # V9780521190213
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Condition: New. Introductory textbook presenting relational methods in machine learning. Num Pages: 278 pages, 50 b/w illus. 100 exercises. BIC Classification: UYQ. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 253 x 173 x 19. Weight in Grams: 654. . 2012. Illustrated. hardcover. . . . . Books ship from the US and Ireland. Seller Inventory # V9780521190213
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Hardcover. Condition: new. Hardcover. What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual properties of different approaches. What is knowledge and how is it represented? This introductory textbook presents relational methods in machine learning. Its clear and precise presentation is ideal for undergraduate computer science students and it will also interest those who study artificial intelligence or machine learning at the graduate level. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780521190213
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