Symbolic machine learning:
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Norwood, NJ
Ablex Publ.
1996
|
| Schriftenreihe: | Briscoe, Garry: A compendium of machine learning
1 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007848181&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XVI, 353 S. Ill., graph. Darst. |
| ISBN: | 1567501788 |
Internformat
MARC
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| 100 | 1 | |a Briscoe, Garry |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Symbolic machine learning |c Garry Briscoe ; Terry Caelli |
| 264 | 1 | |a Norwood, NJ |b Ablex Publ. |c 1996 | |
| 300 | |a XVI, 353 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Briscoe, Garry: A compendium of machine learning |v 1 | |
| 490 | 0 | |a Ablex series in artificial intelligence | |
| 650 | 4 | |a Aprendizaje automático | |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
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| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Caelli, Terry |e Verfasser |4 aut | |
| 830 | 0 | |a Briscoe, Garry: A compendium of machine learning |v 1 |w (DE-604)BV011644389 |9 1 | |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-007848181 | |
Datensatz im Suchindex
| _version_ | 1819301686861103104 |
|---|---|
| adam_text | Contents
I Introduction 1
1 Definitions, Paradigms, Taxonomies 5
1.1 What Is Machine Learning?................................. 5
1.2 Paradigms................................................. 7
1.3 Taxonomies................................................ 8
1.4 Representation of Acquired Concepts....................... 8
1.5 Background Knowledge..................................... 10
1.6 Comparison of Techniques................................. 11
1.7 Knowledge-Level vs. Symbol-Level......................... 11
1.8 Theoretical and Empirical Evaluation..................... 13
II Symbolic Empirical Learning 15
2 Introduction to SEL 17
3 Learning Prom Examples 21
3.1 Description Languages.................................... 21
3.2 Learning As Search....................................... 23
3.3 Single vs. Multiple-concept Learning..................... 24
3.4 Incremental vs. Batch Learning........................... 24
3.5 The Importance of Inductive Bias......................... 25
3.6 The Single Representation Trick.......................... 25
3.7 The Need for Constructive Induction...................... 25
3.8 The Problem of Noisy Data................................ 27
3.9 Source of Instances...................................... 28
3.10 Psychological Evidence................................... 28
vi Contents
4 Decision Trees 31
4.1 Decision Trees as Concept Classifiers..................... 31
4.2 Representational Restrictions............................. 32
4.3 The TDIDT Family Tree .................................... 32
4.4 Evaluation of the TDIDT Method ........................... 32
4.5 Cls—Concept Learning System............................... 33
4.5.1 General Cls Algorithm ............................ 34
4.6 Id3....................................................... 35
4.6.1 Windowing......................................... 35
4.6.2 Problems with Id3................................. 36
4.6.3 Noise, Missing Values, and Pruning................ 37
4.7 Related Systems and Recent Work........................... 38
4.7.1 Acls.............................................. 38
4.7.2 Assistant ........................................ 38
4.7.3 C4, C4.5.......................................... 38
4.7.4 Cart ............................................. 39
4.7.5 Fringe............................................ 39
4.7.6 M5................................................ 41
4.7.7 Mars.............................................. 41
4.7.8 PlsI.............................................. 42
4.7.9 Conditional Rule Generation (Crg) ................ 42
4.7.10 Decision Graphs .................................. 42
4.8 Alternative Test Selection Heuristics..................... 43
4.9 Inclusion of Background Knowledge......................... 45
4.10 Discovery of New Features................................. 45
4.11 Incremental Processing of Examples........................ 45
4.12 Continuous-Valued Attributes.............................. 46
5 Version Spaces 47
5.1 Basic Version Space Algorithm............................. 51
5.2 Discussion of the Version Space Method.................... 52
5.3 Representational Restrictions............................. 52
6 Covering Algorithms 55
6.1 The Aq Star Methodology................................... 55
6.1.1 Simplified Star Algorithm......................... 56
6.1.2 Problem Background Knowledge...................... 57
6.1.3 Generalization Rules.............................. 58
6.2 AqII ..................................................... 59
6.2.1 Aq15 ............................................. 62
6.2.2 Aqtt-15 and Poseidon.............................. 63
6.3 Induce.................................................... 64
6.3.1 The Induce Algorithm ............................. 65
6.4 Rigel..................................................... 67
6.5 Discussion of AQ-Based Methods............................ 68
6.6 Least Generalization...................................... 68
Contents
• ♦
Vil
6.6.1 Plotkin......................................... 68
6.6.2 Algorithm for Least Generalization.............. 69
6.7 Dlg..................................................... 71
6.7.1 The Dlg Algorithm............................... 71
6.7.2 Discussion of Dlg .............................. 72
6.8 Other Least Generalization Systems..................... 72
6.9 Other Covering Systems.................................. 73
6.9.1 Cn2............................................. 73
6.9.2 Decision Lists................................ 73
6.10 Clustering and Numerical Systems........................ 74
7 Inductive Logic Programming 75
7.1 Foil.................................................... 77
7.1.1 The Foil Algorithm.............................. 80
7.1.2 Limitations and Discussion ..................... 81
7.2 Golem .................................................. 82
7.2.1 The Golem Algorithm............................. 87
7.3 Other Recent ILP Systems ............................... 88
8 Inductive Bias 89
9 Conceptual Clustering 95
9.1 Cluster/2............................................... 96
9.1.1 The Cluster/2 Algorithm......................... 98
9.1.2 Cluster/s....................................... 99
9.2 Cobweb .................................................100
9.2.1 Category Utility ...............................101
9.2.2 Representation of Concepts......................102
9.2.3 Operators.......................................103
9.2.4 The Cobweb Algorithm............................103
9.2.5 Discussion of Cobweb.......................... 108
9.2.6 Related Systems ................................108
9.3 Unimem..................................................108
9.3.1 The Unimem Algorithm............................109
9.3.2 Researcher......................................Ill
9.4 Witt....................................................Ill
9.5 Other Conceptual Clustering Systems.....................113
10 Machine Discovery 115
10.1 Am......................................................115
10.1.1 The Architecture of Am..........................116
10.1.2 Discussion of Am................................120
10.2 Eurisko ................................................121
10.3 Bacon ..................................................123
10.3.1 Summary of the Bacon Programs...................123
10.3.2 Detecting Trends and Constants .................124
Contents
viii
10.3.3 Bacon’s Rule-Space Operators....................125
10.3.4 Intrinsic Properties and Common Divisors.......127
10.3.5 Discussion of the Bacon Method.................128
10.3.6 Related Discovery Systems.......................129
10.4 Abacus.................................................129
10.5 Phineas................................................130
10.6 Other Discovery Systems ...............................131
Appendix: Other SEL Topics 133
III Analytical Learning 135
11 Introduction to EBL 137
11.1 EBL and Human Learning.................................139
11.2 Bias and Domain Knowledge..............................139
11.3 Imperfect Domain Theory................................140
11.4 The Utility Problem....................................140
11.5 Operationally..........................................141
11.6 Operationality and Generality..........................141
11.7 Representations and Learning...........................142
12 Composite Rules 143
12.1 Ebg—Explanation-Based Generalization...................143
12.1.1 The Ebg Algorithm..............................147
12.1.2 mEbg—Multiple Example Ebg......................147
12.2 Eggs ..................................................150
12.2.1 The Eggs Algorithm.............................153
12.3 Genesis................................................154
12.4 BAGGER2................................................156
12.5 Equivalence of Algorithms..............................157
12.6 Other Macro-Operator Systems...........................158
13 Search Control Knowledge 159
13.1 Lex2 ..................................................159
13.1.1 MetaLex........................................161
13.2 Prodigy................................................162
13.3 Soar ..................................................164
13.4 Other Search Control Systems...........................166
Appendix: Other EBL Topics 167
IV Exemplars, Case-Based Reasoning, and Analogy 173
14 Exemplar-Based Learning
14.1 IBL...............
177
179
Contents ix
14.1.1 The Ibl Algorithms ...............................179
14.1.2 Similarity Function...............................181
14.2 Protos....................................................181
14.2.1 Protos Classification Algorithm...................183
15 Case-Based Reasoning 185
15.1 Judge.....................................................187
15.2 Chef .....................................................188
Appendix: Other Exemplar, Case-Based Topics 189
16 Learning by Analogy 191
16.1 Diagrammatic View ........................................191
16.2 The Analogy Process.......................................192
16.3 Modes of Analogy .........................................193
16.3.1 Proportional Analogy .............................193
16.3.2 Predictive Analogy................................194
16.3.3 Interpretive Analogy..............................194
16.4 Copycat...................................................195
16.5 Analogy...................................................196
16.6 Derivational Analogy......................................197
16.7 Structure Mapping Theory .................................198
16.8 Pups......................................................199
16.9 Purpose-Directed Analogy..................................200
Appendix: Other Analogy Topics 201
V Integrated Learning Systems 203
17 Introduction to Integrated Systems 207
18 Overly General or Overly Specific Theories 211
18.1 Ioe.......................................................211
18.1.1 Semantic Bias.....................................214
18.1.2 Discussion of the Method..........................215
18.1.3 Vapnik-Chervonenkis Dimension ....................215
18.2 Iou ......................................................216
18.2.1 The Iou Algorithm.................................217
18.3 Incremental Version Space Merging.........................219
18.3.1 The ivsm Algorithm................................221
18.3.2 An Example of the IVSM Method.....................222
18.3.3 Discussion of the ivsm Method.....................223
18.4 Other Systems for Overly General Theories.................224
18.5 Overly Specific Domain Theories...........................224
18.6 Learning by Failing to Explain............................224
18.7 Sierra ...................................................225
x Contents
18.8 Other Systems for Overly Specific Theories.............226
19 Systems for General Theory Revision 227
19.1 Ml-Smart................................................227
19.1.1 The Ml֊Smart Algorithm .........................231
19.1.2 Discussion of the Method........................232
19.2 Focl ..................................................232
19.3 Either..................................................234
19.3.1 An Example of Either ...........................236
19.3.2 Theory for Data Interpretation..................237
19.3.3 Discussion of the Method .......................238
19.4 Forte...................................................238
19.4.1 Inverse Resolution..............................239
19.5 Occam...................................................240
19.6 Other Systems for Theory Revision......................241
19.7 Abduction...............................................241
19.8 Learning Apprentice Systems............................243
19.8.1 Leap............................................243
19.8.2 Disciple........................................244
19.8.3 Odysseus........................................245
19.8.4 Clint-CIA.......................................245
19.9 Knowledge Acquisition Systems...........................246
Appendix: Other Integrated System Topics 247
VI Formal Analysis—Theory 251
20 Machine Learning Theory 253
20.1 Gold....................................................253
20.2 Valiant.................................................254
20.3 Blumer Bound............................................255
20.4 Bias....................................................256
20.5 DeMorgan’s Rules........................................259
20.6 Valiant’s Algorithm for k-CNF...........................259
20.7 Vapnik-Chervonenkis Dimension...........................260
20.8 Example PAC Analysis ...................................261
20.9 Structural Domains and Learnability.....................262
20.10 Average-Case Analysis..................................262
Appendix: Other Formal Theory Topics 263
VII Appendices 265
A Glossary 267
Contents
xi
B Electronic Information Sources 275
B.l Information Services..........................................275
B.2 Subscription Lists ...........................................276
B.2.1 Machine Learning List . . . . .....................276
B.2.2 Journal of Artificial Intelligence Research...........276
B.2.3 ILP Newsletter.............. . *......................276
B.2.4 MLnet.................................................277
B.3 Archives......................................................277
B.3.1 UCI Repository of Machine Learning Databases . . . . 277
B.3.2 neuroprose....................*.......................277
B.3.3 CMU AI Repository.....................................278
B.3.4 Mobal ................................................278
B.3.5 Foil..................................................278
B.3.6 Others................................................278
B.4 Conferences............................*.......................279
References 281
Credits 327
Author Index 331
Subject Index 339
|
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| id | DE-604.BV011644407 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T10:16:11Z |
| institution | BVB |
| isbn | 1567501788 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007848181 |
| oclc_num | 629805798 |
| open_access_boolean | |
| owner | DE-739 DE-473 DE-BY-UBG |
| owner_facet | DE-739 DE-473 DE-BY-UBG |
| physical | XVI, 353 S. Ill., graph. Darst. |
| publishDate | 1996 |
| publishDateSearch | 1996 |
| publishDateSort | 1996 |
| publisher | Ablex Publ. |
| record_format | marc |
| series | Briscoe, Garry: A compendium of machine learning |
| series2 | Briscoe, Garry: A compendium of machine learning Ablex series in artificial intelligence |
| spellingShingle | Briscoe, Garry Caelli, Terry Symbolic machine learning Briscoe, Garry: A compendium of machine learning Aprendizaje automático Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Symbolic machine learning |
| title_auth | Symbolic machine learning |
| title_exact_search | Symbolic machine learning |
| title_full | Symbolic machine learning Garry Briscoe ; Terry Caelli |
| title_fullStr | Symbolic machine learning Garry Briscoe ; Terry Caelli |
| title_full_unstemmed | Symbolic machine learning Garry Briscoe ; Terry Caelli |
| title_short | Symbolic machine learning |
| title_sort | symbolic machine learning |
| topic | Aprendizaje automático Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Aprendizaje automático Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007848181&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV011644389 |
| work_keys_str_mv | AT briscoegarry symbolicmachinelearning AT caelliterry symbolicmachinelearning |