Relational knowledge discovery /
Müller, M. E. 1970-
Relational knowledge discovery / M.E. Müller. - New York : Cambridge University Press, 2012. - 1 online resource - Lecture notes on machine learning . - Lecture notes on machine learning. .
Includes bibliographical references and index.
Cover; Relational Knowledge Discovery; Title; Copyright; Contents; About this book; What it is about; How it is organised; Thanks to:; Chapter 1: Introduction; 1.1 Motivation; 1.1.1 Different kinds of learning; 1.1.2 Applications; 1.2 Related disciplines; 1.2.1 Codes and compression; 1.2.2 Information theory; 1.2.3 Minimum description length; 1.2.4 Kolmogorov complexity; 1.2.5 Probability theory; Conclusion; Chapter 2: Relational knowledge; 2.1 Objects and their attributes; 2.1.1 Collections of things: sets; 2.1.2 Properties of things: relations; 2.1.3 Special properties of relations.
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.
9781139518185 (electronic bk.) 1139518186 (electronic bk.) 9781139047869 (electronic bk.) 1139047868 (electronic bk.) 1280773812 9781280773815 9781139516334 1139516337 (Cloth) (Cloth)
9786613684585
Computational learning theory.
Machine learning.
Relational databases.
Théorie de l'apprentissage informatique.
Apprentissage automatique.
Bases de données relationnelles.
COMPUTERS--Enterprise Applications--Business Intelligence Tools.
COMPUTERS--Intelligence (AI) & Semantics.
Computational learning theory.
Machine learning.
Relational databases.
Electronic books.
Q325.7
006.3/1
Relational knowledge discovery / M.E. Müller. - New York : Cambridge University Press, 2012. - 1 online resource - Lecture notes on machine learning . - Lecture notes on machine learning. .
Includes bibliographical references and index.
Cover; Relational Knowledge Discovery; Title; Copyright; Contents; About this book; What it is about; How it is organised; Thanks to:; Chapter 1: Introduction; 1.1 Motivation; 1.1.1 Different kinds of learning; 1.1.2 Applications; 1.2 Related disciplines; 1.2.1 Codes and compression; 1.2.2 Information theory; 1.2.3 Minimum description length; 1.2.4 Kolmogorov complexity; 1.2.5 Probability theory; Conclusion; Chapter 2: Relational knowledge; 2.1 Objects and their attributes; 2.1.1 Collections of things: sets; 2.1.2 Properties of things: relations; 2.1.3 Special properties of relations.
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.
9781139518185 (electronic bk.) 1139518186 (electronic bk.) 9781139047869 (electronic bk.) 1139047868 (electronic bk.) 1280773812 9781280773815 9781139516334 1139516337 (Cloth) (Cloth)
9786613684585
Computational learning theory.
Machine learning.
Relational databases.
Théorie de l'apprentissage informatique.
Apprentissage automatique.
Bases de données relationnelles.
COMPUTERS--Enterprise Applications--Business Intelligence Tools.
COMPUTERS--Intelligence (AI) & Semantics.
Computational learning theory.
Machine learning.
Relational databases.
Electronic books.
Q325.7
006.3/1