Inductive Logic Programming: Techniques and Applications

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Product Details

Table of Contents

Foreword
Preface
1 Introduction 3
1.1 Inductive concept learning 3
1.2 Background knowledge 10
1.3 Language bias 11
1.4 Inductive logic programming 13
1.5 Imperfect data 15
1.6 Applications of ILP 17
2 Inductive logic programming 23
2.1 Logic programming and deductive database terminology 23
2.2 Empirical ILP 28
2.3 Interactive ILP 31
2.4 Structuring the hypothesis space 33
3 Basic ILP techniques 39
3.1 Generalization techniques 39
3.2 Specialization techniques 53
4 An overview of empirical ILP systems 67
4.1 An overview of FOIL 67
4.2 An overview of GOLEM 74
4.3 An overview of MOBAL 76
4.4 Other empirical ILP systems 77
5 LINUS: Using attribute-value learners in an ILP framework 81
5.1 An outline of the LINUS environment 81
5.2 Attribute-value learning 84
5.3 Using background knowledge in learning 95
5.4 The LINUS algorithm 99
5.5 The complexity of learning constrained DHDB clauses 108
5.6 Weakening the language bias 111
5.7 Learning determinate clauses with DINUS 114
6 Experiments in learning relations with LINUS 123
6.1 Experimental setup 123
6.2 Learning family relationships 124
6.3 Learning the concept of an arch 126
6.4 Learning rules that govern card sequences 129
6.5 Learning illegal chess endgame positions 133
7 ILP as search of refinement graphs 137
7.1 ILP as search of program clauses 137
7.2 Defining refinement graphs 139
7.3 A MIS refinement operator 140
7.4 Refinement operators for FOIL and LINUS 141
7.5 Costs of searching refinement graphs 147
7.6 Comparing FOIL and LINUS 149
8 Handling imperfect data in ILP 153
8.1 Types of imperfect data 153
8.2 Handling missing values 155
8.3 Handling noise in attribute-value learning 156
8.4 Handling noise in ILP 158
8.5 Heuristics for noise-handling in empirical ILP 162
8.6 Probability estimates 165
8.7 Heuristics at work 167
9 mFOIL: Extending noise-handling in FOIL 173
9.1 Search space 173
9.2 Search heuristics 176
9.3 Search strategy and stopping criteria 178
9.4 Implementation details 179
10 Experiments in learning relations from noisy examples 183
10.1 Introducing noise in the examples 184
10.2 Experiments with LINUS 185
10.3 Experiments with mFOIL 189
11 Learning rules for early diagnosis of rheumatic diseases 199
11.1 Diagnostic problem and experimental data 200
11.2 Medical background knowledge 200
11.3 Experiments and results 203
12 Finite element mesh design 217
12.1 The finite element method 217
12.2 The learning problem 219
12.3 Experimental setup 221
12.4 Results and discussion 223
13 Learning qualitative models of dynamic systems 227
13.1 Qualitative modeling 228
13.2 An experiment in learning qualitative models 233
13.3 Related work 239
14 Other ILP applications 243
14.1 Predicting protein secondary structure 243
14.2 Modeling structure-activity relationships 247
14.3 Learning diagnostic rules from qualitative models 252
Bibliography 263
Index 287
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