Relational Knowledge Discovery by Müller (29 results)

Condition: very_good.

Condition: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9780521122047.

Condition: New.

Condition: New.

Condition: Very Good.

Condition: New. In.

Condition: New.

Condition: New. pp. 280.

Condition: New. Introductory textbook presenting relational methods in machine learning. Num Pages: 280 pages, 50 b/w illus. 100 exercises. BIC Classification: UYQ. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 244 x 171 x 15. Weight in Grams: 514. . 2012. paperback. . . . . Books ship from the US and Ireland. …

Condition: New.

Condition: New. In.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

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. …

Condition: New. pp. 278 1st Edition.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: As New. Unread book in perfect condition.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Condition: Sehr gut. Zustand: Sehr gut | Seiten: 278 | Sprache: Englisch | Produktart: Bücher | Introductory textbook presenting relational methods in machine learning.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condition: New. Print on Demand pp. 280 50 Illus.

Condition: New. PRINT ON DEMAND pp. 280.

Paperback. Condition: new. Paperback. 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.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 artifi.…

Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condition: New. Print on Demand pp. 278 50 Illus.

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.…

Condition: New. PRINT ON DEMAND pp. 278.

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 artifi.…