Research Catalog

Title

Relational knowledge discovery / M.E. Müller.

Author

Müller, M. E. (Martin E.), 1970-

Publication

Cambridge ; New York : Cambridge University Press, 2012.

Items in the Library & Off-site

Details

Description

vi, 271 p. : ill.; 26 cm.

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"--

Series Statement

Lecture notes on machine learning

Subject

• Computational learning theory
• Machine learning
• Relational databases

Bibliography (note)

• Includes bibliographical references and index.

ISBN

• 9780521190213 (hardback)
• 0521190215 (hardback)
• 9780521122047 (paperback)
• 052112204X (paperback)

LCCN

2011049968

OCLC

• ocn773533818
• SCSB-9147600

Owning Institutions

Princeton University Library