UID:
edocfu_9959228376702883
Format:
1 online resource (vi, 271 pages) :
,
digital, PDF file(s).
ISBN:
1-107-22298-2
,
1-280-77381-2
,
9786613684585
,
1-139-51725-2
,
1-139-51468-7
,
1-139-04786-8
,
1-139-51375-3
,
1-139-51633-7
,
1-139-51818-6
Series Statement:
Lecture notes on machine learning Relational knowledge discovery
Content:
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.
Note:
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
,
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
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2.1.4 Information systems2.1.5 Structured sets; 2.1.6 Probabilities; 2.1.7 Relation algebra; 2.2 Knowledge structures; 2.2.1 Concepts, equivalence relations, and knowledge; 2.2.2 Operations on equivalence relations; 2.2.3 Indiscernability and knowledge; Chapter 3: From data to hypotheses; 3.1 Representation; 3.2 Changing the representation; 3.2.1 Linear separability; 3.3 Samples; 3.4 Evaluation of hypotheses; 3.4.1 Error sets and error measures; 3.4.2 Precision, accuracy, and others; 3.5 Learning; 3.6 Bias; 3.7 Overfitting; 3.8 Summary; Chapter 4: Clustering; 4.1 Concepts as sets of objects
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4.2 k-nearest neighbours4.3 k-means clustering; 4.4 Incremental concept formation; 4.5 Relational clustering; Chapter 5: Information gain; 5.1 Entropy; 5.2 Information and information gain; 5.2.1 Entropy; 5.2.2 Information; 5.3 Induction of decision trees; 5.3.1 Hunt's classifier trees and Quinlan's ID3; 5.4 Gain again; 5.5 Pruning; 5.5.1 Reduced error pruning; 5.5.2 Rule-based post-pruning; 5.6 Conclusion; Chapter 6: Rough set theory; 6.1 Knowledge and discernability; 6.2 Rough knowledge; 6.2.1 Rough approximations; 6.2.2 Degrees of roughness; 6.3 Rough knowledge structures
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6.4 Relative knowledge6.5 Knowledge discovery; 6.5.1 Utility; 6.5.2 Attribute significance; 6.6 Conclusion; Chapter 7: Inductive logic learning; 7.1 From information systems to logic programs; 7.1.1 Functions and relations; 7.1.2 Semantics of first order logic; 7.1.3 Deduction; 7.2 Horn logic; 7.2.1 Logic programs; 7.2.2 Induction of logic programs; 7.2.3 Entailment, generality, and subsumption; 7.3 Heuristic rule induction; 7.3.1 Refinement operators on H; 7.3.2 Heuristic refinement; 7.4 Inducing Horn theories from data; 7.4.1 Syntactic generalisation revisited; 7.4.2 Inverting resolution
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7.4.3 Semantic biases7.4.4 Inverted entailment; 7.5 Summary; Chapter 8: Learning and ensemble learning; 8.1 Learnability; 8.1.1 Probably approximately correct learning; 8.1.2 Learnability and learning algorithms; 8.2 Decomposing the learning problem; 8.2.1 Bagging; 8.3 Improving by focusing on errors; 8.4 A relational view on ensemble learning; 8.4.1 Dividing the sample set; 8.4.2 Focusing on errors; 8.5 Summary; Chapter 9: The logic of knowledge; 9.1 Knowledge representation; 9.2 Learning; 9.2.1 Clustering; 9.2.2 Decision trees; 9.2.3 Rough sets; 9.2.4 Inductive logic programming
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9.3 Summary
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English
Additional Edition:
ISBN 0-521-12204-X
Additional Edition:
ISBN 0-521-19021-5
Language:
English
