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Relational knowledge discovery / M.E. Müller.

By: Material type: TextTextSeries: Lecture notes on machine learningPublisher: Cambridge ; New York : Cambridge University Press, 2012Description: vi, 271 pages. : illustrations ; 26 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780521190213 (hardback)
  • 0521190215 (hardback)
  • 9780521122047 (paperback)
  • 052112204X (paperback)
Subject(s): DDC classification:
  • 006.31 23 M.M.R
LOC classification:
  • Q325.7 .M85 2012
Online resources:
Contents:
1. Introduction; 2. Relational knowledge; 3. From data to hypotheses; 4. Clustering; 5. Information gain; 6. Knowledge and relations; 7. Rough set theory; 8. Inductive logic learning; 9. Ensemble learning; 10. The logic of knowledge; 11. Indexes and bibliography; Bibliography; Index.
Summary: "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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Holdings
Item type Current library Collection Call number Status Date due Barcode
Books Books Main library A2 Computers & Information Technology ( Information systems ) 006.31 M.M.R (Browse shelf(Opens below)) Available 00009616

Includes bibliographical references (p. 261-266) and index.

1. Introduction; 2. Relational knowledge; 3. From data to hypotheses; 4. Clustering; 5. Information gain; 6. Knowledge and relations; 7. Rough set theory; 8. Inductive logic learning; 9. Ensemble learning; 10. The logic of knowledge; 11. Indexes and bibliography; Bibliography; Index.

"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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