Inductive logic programming: techniques and applications
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York [u.a.]
Ellis Horwood
1994
|
| Edition: | 1. publ. |
| Series: | Ellis Horwood series in artificial intelligence
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIX, 293 S. graph. Darst. |
| ISBN: | 0134578708 |
Staff View
MARC
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| 100 | 1 | |a Lavrač, Nada |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Inductive logic programming |b techniques and applications |c Nada Lavrač and Sašo Džeroski |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a New York [u.a.] |b Ellis Horwood |c 1994 | |
| 300 | |a XIX, 293 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 0 | |a Ellis Horwood series in artificial intelligence | |
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Record in the Search Index
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|---|---|
| adam_text | Contents
Foreword xi
Preface xv
I Introduction to ILP 1
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
3.1.1 Relative least general generalization...........40
3.1.2 Inverse resolution .............................43
3.1.3 A unifying framework for generalization ........48
3.2 Specialization techniques..............................53
3.2.1 Top-down search of refinement graphs............53
3.2.2 A unifying framework for specialization.........57
v
vi Contents
II Empirical ILP 65
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
5.2.1 An example learning problem.......................84
5.2.2 ASSISTANT ........................................85
5.2.3 NEWGEM............................................88
5.2.4 CN2...............................................93
5.3 Using background knowledge in learning...................95
5.3.1 Using background knowledge
in attribute-value learning.......................95
5.3.2 Transforming ILP problems to propositional form 97
5.4 The LINUS algorithm .....................................99
5.4.1 Pre-processing of training examples..............100
5.4.2 Post-processing..................................102
5.4.3 An example run of LINUS..........................103
5.4.4 Language bias in LINUS...........................105
5.5 The complexity of learning constrained DHDB clauses . . 108
5.6 Weakening the language bias.............................Ill
5.6.1 The ¿-determinacy bias...........................Ill
5.6.2 An example determinate definition................113
5.7 Learning determinate clauses with DINUS.................114
5.7.1 Learning non-recursive determinate DDB clauses 115
5.7.2 Learning recursive determinate DDB clauses . . . 120
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
Contents vii
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
7.4.1 Refinement operator for FOIL...................141
7.4.2 Refinement operator for LINUS..................144
7.5 Costs of searching refinement graphs .................147
7.6 Comparing FOIL and LINUS..............................149
III Handling Imperfect Data in ILP 151
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
8.7.1 The training set and its partitions...........167
8.7.2 Search heuristics at work......................168
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
viii Contents
IV Applications of ILP 197
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
11.3.1 Learning from the entire training set..........205
11.3.2 Medical evaluation of diagnostic rules.........206
11.3.3 Performance on unseen examples.................210
11.4 Discussion...........................................214
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
13.1.1 The QSIM formalism.............................228
13.1.2 The U-tube system..............................229
13.1.3 Formulating QSIM in logic......................231
13.2 An experiment in learning qualitative models.........233
13.2.1 Experimental setup.............................234
13.2.2 Results........................................236
13.2.3 Comparison with other ILP systems..............238
13.3 Related work.........................................239
13.4 Discussion...........................................240
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
14.3.1 The KARDIO methodology.........................253
14.3.2 Learning temporal diagnostic rules ............257
14.4 Discussion...........................................262
Contents ix
Bibliography 263
Index 287
|
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| discipline | Informatik Mathematik |
| edition | 1. publ. |
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| isbn | 0134578708 |
| language | English |
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| physical | XIX, 293 S. graph. Darst. |
| publishDate | 1994 |
| publishDateSearch | 1994 |
| publishDateSort | 1994 |
| publisher | Ellis Horwood |
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| series2 | Ellis Horwood series in artificial intelligence |
| spelling | Lavrač, Nada Verfasser aut Inductive logic programming techniques and applications Nada Lavrač and Sašo Džeroski 1. publ. New York [u.a.] Ellis Horwood 1994 XIX, 293 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Ellis Horwood series in artificial intelligence ILP inriac Induction (logique) ram Programmation logique ram logique induction inriac programmation logique inriac Logic programming Logische Programmierung (DE-588)4195096-3 gnd rswk-swf Logische Programmierung (DE-588)4195096-3 s DE-604 Džeroski, Sašo 1968- Verfasser (DE-588)121071723 aut Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006388238&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lavrač, Nada Džeroski, Sašo 1968- Inductive logic programming techniques and applications ILP inriac Induction (logique) ram Programmation logique ram logique induction inriac programmation logique inriac Logic programming Logische Programmierung (DE-588)4195096-3 gnd |
| subject_GND | (DE-588)4195096-3 |
| title | Inductive logic programming techniques and applications |
| title_auth | Inductive logic programming techniques and applications |
| title_exact_search | Inductive logic programming techniques and applications |
| title_full | Inductive logic programming techniques and applications Nada Lavrač and Sašo Džeroski |
| title_fullStr | Inductive logic programming techniques and applications Nada Lavrač and Sašo Džeroski |
| title_full_unstemmed | Inductive logic programming techniques and applications Nada Lavrač and Sašo Džeroski |
| title_short | Inductive logic programming |
| title_sort | inductive logic programming techniques and applications |
| title_sub | techniques and applications |
| topic | ILP inriac Induction (logique) ram Programmation logique ram logique induction inriac programmation logique inriac Logic programming Logische Programmierung (DE-588)4195096-3 gnd |
| topic_facet | ILP Induction (logique) Programmation logique logique induction programmation logique Logic programming Logische Programmierung |
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