Case-based reasoning: a textbook
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2013
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| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XVIII, 546 S. Ill., graph. Darst. |
| ISBN: | 9783642401664 9783642401671 |
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MARC
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| 245 | 1 | 0 | |a Case-based reasoning |b a textbook |c Michael M. Richter ; Rosina O. Weber |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2013 | |
| 300 | |a XVIII, 546 S. |b Ill., graph. Darst. | ||
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Record in the Search Index
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CONTENTS
PART I BASICS
1 INTRODUCTION 3
1.1 ABOUT THIS CHAPTER 3
1.2 GENERAL ASPECTS 3
1.2.1 A BIT OF THE HISTORY 4
1.3 INTENDED READERS 6
1.4 OUTLINE OF THE BOOK 7
1.4.1 PART I: BASICS 7
1.4.2 PART II: CORE METHODS 9
1.4.3 PART III: ADVANCED ELEMENTS 11
1.4.4 PART IV: COMPLEX KNOWLEDGE SOURCES 12
1.4.5 PART V: ADDITIONS 13
1.4.6 CHAPTER STRUCTURE 14
REFERENCES 15
2 BASIC CBR ELEMENTS 17
2.1 ABOUT THIS CHAPTER 17
2.2 GENERAL ASPECTS 17
2.3 CASE-BASED REASONING 17
2.4 EXPERIENCES AND CASES 18
2.4.1 PARTS OF A CASE 20
2.4.2 PROBLEMS 21
2.4.3 SOLUTION TYPES 22
2.5 CASE REPRESENTATIONS 23
2.5.1 HOW CASES ARE REPRESENTED 23
2.6 CASE BASES 24
2.6.1 HOW ARE CASES ORGANISED? 25
2.7 SIMILARITY AND RETRIEVAL 26
2.8 REUSE AND ADAPTATION 30
2.9 MODELS OF CBR 31
2.9.1 CBR PROCESS MODEL 32
VII
HTTP://D-NB.INFO/1038181771
VIII CONTENTS
2.9.2 CBR KNOWLEDGE MODEL 34
2.10 TOOLS 37
2.11 CHAPTER SUMMARY 38
2.12 BACKGROUND INFORMATION 38
2.13 EXERCISES 39
REFERENCES 40
3 EXTENDED VIEW 41
3.1 ABOUT THIS CHAPTER 41
3.2 GENERAL ASPECTS 41
3.2.1 E-COMMERCE 42
3.2.2 RECOMMENDER SYSTEMS 46
3.3 EXTENDED MODEL 47
3.4 MORE GENERALIZATIONS 49
3.5 TOOLS 50
3.6 CHAPTER SUMMARY 51
3.7 BACKGROUND INFORMATION 51
3.8 EXERCISES 51
REFERENCES 52
4 APPLICATION EXAMPLES 53
4.1 ABOUT THIS CHAPTER 53
4.2 GENERAL ASPECTS 53
4.3 ANALYTIC TASKS 55
4.3.1 CLASSIFICATION 56
4.3.2 DIAGNOSIS 57
4.3.3 PREDICTION 62
4.4 SYNTHETIC TASKS 64
4.4.1 CONFIGURATION 64
4.4.2 PLANNING 66
4.4.3 DESIGN 69
4.5 ORGANISATION-ORIENTED APPLICATIONS 70
4.5.1 CALL CENTRES 70
4.5.2 E-COMMERCE 72
4.5.3 KNOWLEDGE MANAGEMENT 75
4.5.4 LAW 78
4.6 COMPLEX KNOWLEDGE SOURCES 80
4.6.1 TEXTS 80
4.6.2 IMAGES 81
4.7 CHAPTER SUMMARY 82
4.8 BACKGROUND INFORMATION 82
4.9 EXERCISES 83
REFERENCES 84
CONTENTS IX
PART II CORE METHODS
5 CASE REPRESENTATIONS 87
5.1 ABOUT THIS CHAPTER 87
5.2 GENERAL ASPECTS 87
5.2.1 REPRESENTATION LAYERS 87
5.2.2 COMPLETENESS AND EFFICIENCY 92
5.2.3 FLAT ATTRIBUTE-VALUE REPRESENTATION 93
5.2.4 COMPLEX REPRESENTATIONS IN GENERAL 95
5.3 TOOLS 109
5.4 CHAPTER SUMMARY 110
5.5 BACKGROUND INFORMATION 110
5.6 EXERCISES 110
REFERENCES ILL
6 BASIC SIMILARITY TOPICS 113
6.1 ABOUT THIS CHAPTER 113
6.2 GENERAL ASPECTS 113
6.3 SIMILARITY AND CASE REPRESENTATIONS 114
6.3.1 MATHEMATICAL MODELS OF SIMILARITY 115
6.3.2 MEANING OF SIMILARITY 115
6.4 TYPES OF SIMILARITY MEASURES 125
6.4.1 COUNTING SIMILARITIES 126
6.4.2 METRIC SIMILARITIES 129
6.4.3 COMPARISONS 130
6.4.4 STRUCTURED SIMILARITIES AND SYMBOLIC ARGUMENTS . 130
6.4.5 TRANSFORMATIONAL SIMILARITIES 133
6.5 THE LOCAL-GLOBAL PRINCIPLE FOR SIMILARITY MEASURES 133
6.5.1 WEIGHTED MEASURES 134
6.5.2 LOCAL MEASURES 135
6.5.3 GLOBAL ASPECTS 138
6.5.4 WEIGHTS 138
6.6 VIRTUAL ATTRIBUTES 141
6.7 WHICH SIMILARITY MEASURE SHOULD I USE? 142
6.8 TOOLS 144
6.9 CHAPTER SUMMARY 145
6.10 BACKGROUND INFORMATION 145
6.11 EXERCISES 146
REFERENCES 147
7 COMPLEX SIMILARITY TOPICS 149
7.1 ABOUT THIS CHAPTER 149
7.2 GRAPH REPRESENTATIONS AND GRAPH SIMILARITIES 149
7.2.1 GRAPH ISOMORPHISM 149
7.2.2 SUBGRAPH ISOMORPHISM 150
7.3 LARGEST COMMON SUBGRAPHS 150
X
CONTENTS
7.3.1 EDIT OPERATIONS 151
7.4 TAXONOMIC SIMILARITIES 153
7.5 SIMILARITIES FOR OBJECT-ORIENTED REPRESENTATIONS 156
7.6 MANY-VALUED ATTRIBUTES 158
7.7 SIMILARITY FOR PROCESSES AND WORKFLOWS 159
7.7.1 SIMILARITIES FOR TIME SERIES 161
7.8 TOOLS 162
7.9 CHAPTER SUMMARY 162
7.10 BACKGROUND INFORMATION 163
7.11 EXERCISES 163
REFERENCES 164
8 RETRIEVAL 167
8.1 ABOUT THIS CHAPTER 167
8.2 GENERAL ASPECTS 167
8.3 THE RETRIEVAL TASK 168
8.3.1 RETRIEVAL ERRORS 169
8.4 BASIC RETRIEVAL METHODS 170
8.4.1 QUERY GENERATION 170
8.4.2 FILTERING 170
8.4.3 SEQUENTIAL RETRIEVAL 171
8.4.4 TWO-LEVEL RETRIEVAL 172
8.4.5 GEOMETRIC METHODS 173
8.4.6 VORONOI DIAGRAMS AND K-NEAREST NEIGHBOURS 173
8.4.7 GEOMETRIC APPROXIMATION 175
8.4.8 GEOMETRIC FILTERING 176
8.4.9 INDEX-BASED RETRIEVAL 177
8.4.10 KD-TREES 178
8.4.11 INTEGRATION WITH DECISION TREES 183
8.5 TOOLS 185
8.6 CHAPTER SUMMARY 185
8.7 BACKGROUND INFORMATION 185
8.8 EXERCISES 186
REFERENCES 186
9 ADAPTATION 189
9.1 ABOUT THIS CHAPTER 189
9.2 GENERAL ASPECTS 189
9.3 RULES 191
9.3.1 PRECONDITIONS 192
9.3.2 ACTIONS 192
9.3.3 TYPES OF RULES 193
9.3.4 INTEGRATING COMPLETION AND ADAPTATION RULES 197
9.4 ADAPTATION TYPES 198
9.5 THE ADAPTATION PROCESS 199
9.5.1 ADAPTATION SEQUENCES 200
CONTENTS XI
9.5.2 ADAPTATION PLANNING 202
9.5.3 LEARNING HEURISTICS 209
9.5.4 ADAPTATION IN MORE COMPLEX SITUATIONS 210
9.6 ADAPTATION USING SEVERAL CASES 210
9.6.1 SIMPLE NUMERICAL ADAPTATIONS 210
9.7 ADAPTATIONS USING THE SOLUTION PROCESS 213
9.8 QUALITY ISSUES 216
9.9 KNOWLEDGE IN THE ADAPTATION CONTAINER 216
9.10 WHEN SHOULD ADAPTATION BE CONSIDERED? 217
9.11 TOOLS 217
9.12 CHAPTER SUMMARY 217
9.13 BACKGROUND INFORMATION 218
9.14 EXERCISES 218
REFERENCES 219
10 EVALUATION, REVISION, AND LEARNING 221
10.1 ABOUT THIS CHAPTER 221
10.2 GENERAL ASPECTS 221
10.2.1 THE PURPOSE 221
10.2.2 PRINCIPAL ASPECTS 221
10.3 EVALUATION 223
10.4 REVISION 224
10.5 LEARNING 226
10.5.1 OVERFITTING AND UNDERFITTING 228
10.6 LEARNING TO FILL AND MODIFY KNOWLEDGE CONTAINERS 229
10.6.1 THE VOCABULARY CONTAINER 229
10.6.2 THE CASE BASE CONTAINER 229
10.6.3 THE SIMILARITY CONTAINER 234
10.6.4 THE ADAPTATION CONTAINER 238
10.7 APPLYING MACHINE LEARNING METHODS 238
10.7.1 REGRESSION LEARNING 238
10.7.2 ARTIFICIAL NEURAL NETWORKS 239
10.7.3 GENETIC ALGORITHMS 239
10.7.4 REINFORCEMENT LEARNING 241
10.7.5 CLUSTERING METHODS 241
10.7.6 BAYESIAN LEARNING 242
10.8 TOOLS 242
10.9 CHAPTER SUMMARY 243
10.10 BACKGROUND INFORMATION 243
10.11 EXERCISES 244
REFERENCES 245
11 DEVELOPMENT AND MAINTENANCE 247
11.1 ABOUT THIS CHAPTER 247
11.2 GENERAL ASPECTS 247
11.3 DEVELOPMENT 248
XII CONTENTS
11.3.1 PROBLEM FORMULATION 248
11.3.2 FINDING AND GETTING DATA, PREPROCESSING 250
11.3.3 CASE ACQUISITION 251
11.3.4 PROTOTYPES AND EVALUATION 251
11.3.5 THE KNOWLEDGE CONTAINERS 252
11.3.6 WHICH ADDITIONAL METHODS CAN A CBR SYSTEM HAVE? . 254
11.3.7 SYSTEMATIC DEVELOPMENT OF CBR SYSTEMS 254
11.3.8 IMPLEMENTATION ASPECTS 257
11.3.9 COMBINING CBR WITH OTHER TECHNIQUES 257
11.4 MAINTENANCE 260
11.4.1 CHANGED ENVIRONMENT AND TECHNIQUES 261
11.4.2 MAINTENANCE AND KNOWLEDGE CONTAINERS 263
11.4.3 SYSTEMATIC SYSTEM MAINTENANCE 266
11.5 TOOLS 270
11.6 CHAPTER SUMMARY 271
11.7 BACKGROUND INFORMATION 271
11.8 EXERCISES 272
REFERENCES 272
PART III ADVANCED ELEMENTS
12 ADVANCED CBR ELEMENTS 277
12.1 ABOUT THIS CHAPTER 277
12.2 DISCUSSION OF THE RELATIONS BETWEEN CONTAINERS 277
12.3 CONTEXTS 281
12.3.1 GENERALITIES 281
12.3.2 DIFFERENT CONTEXTS 282
12.3.3 CONTEXTS AND KNOWLEDGE CONTAINERS 284
12.4 ONTOLOGIES 285
12.4.1 ONTOLOGIES IN CBR 286
12.5 CBR SYSTEMS 288
12.5.1 CASE PROPERTIES 289
12.5.2 CASE BASE PROPERTIES 290
12.5.3 CONDITIONS 291
12.5.4 CORRECTNESS AND PROVENANCE 293
12.5.5 DISTRIBUTED CASE BASES 295
12.6 TOOLS 296
12.7 CHAPTER SUMMARY 296
12.8 BACKGROUND INFORMATION 296
12.9 EXERCISES 297
REFERENCES 297
13 ADVANCED SIMILARITY TOPICS 299
13.1 ABOUT THIS CHAPTER 299
13.1.1 FOUNDATIONS 299
13.1.2 FORMAL ASPECTS 300
CONTENTS XIII
13.1.3 MEANING AND SEMANTICS 300
13.1.4 SUBJECTIVITY 303
13.1.5 DISCUSSION OF THE AXIOMS FOR SIMILARITY 306
13.1.6 FIRST-AND SECOND-ORDER SIMILARITIES 308
13.2 MISCELLANEOUS TOPICS 310
13.2.1 NONFUNCTIONAL ASPECTS 310
13.2.2 TOP-DOWN VERSUS BOTTOM-UP 310
13.2.3 JUMPS AND NOISE 312
13.3 FUNCTIONAL DEPENDENCY, UNKNOWN AND REDUNDANT VALUES . . 312
13.3.1 FUNCTIONAL DEPENDENCY 312
13.3.2 UNKNOWN VALUES 313
13.3.3 REDUNDANT VALUES 313
13.4 ADDITIONAL PROBLEMS 314
13.4.1 SIMILARITY AND EXPLANATIONS 314
13.4.2 SIMILARITY AND LOGICAL INFERENCE 314
13.5 THE KNOWLEDGE CONTAINED IN THE MEASURES 315
13.6 TOOLS 317
13.7 CHAPTER SUMMARY 317
13.8 BACKGROUND INFORMATION 317
13.9 EXERCISES 318
REFERENCES 318
14 ADVANCED RETRIEVAL 321
14.1 ABOUT THIS CHAPTER 321
14.2 GENERAL ASPECTS 321
14.2.1 CASE RETRIEVAL NETS 321
14.2.2 FISH AND SHRINK 326
14.2.3 PROTOS: ANOTHER TWO-STEP RETRIEVAL 330
14.2.4 FUZZY RETRIEVAL 331
14.2.5 COMPARISON 331
14.2.6 REDUCING THE SEARCH SPACE AND PREPROCESSING 331
14.3 SIMILARITY DIVERSITY 334
14.4 WHICH RETRIEVAL METHOD SHOULD I USE? 336
14.5 TOOLS 336
14.6 CHAPTER SUMMARY 336
14.7 BACKGROUND INFORMATION 337
14.8 EXERCISES 337
REFERENCES 338
15 UNCERTAINTY 339
15.1 ABOUT THIS CHAPTER 339
15.2 GENERAL ASPECTS 339
15.3 UNCERTAINTY CONCEPTS AND METHODS 340
15.3.1 ROUGH SETS 340
15.3.2 FUZZY SETS 342
15.3.3 BASIC ELEMENTS IN FUZZY SET THEORY 344
XIV CONTENTS
15.4 FUZZY SETS AND CBR 348
15.4.1 GENERAL RELATIONS 348
15.4.2 FUZZY CASES 348
15.4.3 SIMILARITIES 348
15.4.4 COMPARISONS 350
15.5 POSSIBILITY, NECESSITY, AND CBR 352
15.5.1 GENERAL 352
15.6 TOOLS 354
15.7 CHAPTER SUMMARY 354
15.8 BACKGROUND INFORMATION 355
15.9 EXERCISES 355
REFERENCES 355
16 PROBABILITIES 357
16.1 ABOUT THIS CHAPTER 357
16.2 GENERAL ASPECTS 357
16.2.1 FROM PROBABILITIES TO MEASURES 358
16.2.2 FROM SIMILARITIES TO PROBABILITIES 363
16.3 BAYESIAN REASONING 365
16.3.1 DYNAMICS 366
16.3.2 USING THE NETS 368
16.3.3 STOCHASTIC PROCESSES 368
16.4 TOOLS 370
16.5 CHAPTER SUMMARY 370
16.6 BACKGROUND INFORMATION 371
16.7 EXERCISES 371
REFERENCES 372
PART IV COMPLEX KNOWLEDGE SOURCES
17 TEXTUAL CBR 375
17.1 ABOUT THIS CHAPTER 375
17.2 GENERAL 375
17.2.1 TEXT, STRUCTURE, AND LEVELS 376
17.2.2 TEXT PROPERTIES 380
17.2.3 PROBLEMS IN UNDERSTANDING TEXT 382
17.3 THE VOCABULARY CONTAINER 383
17.3.1 TEXT PROCESSING 384
17.3.2 N-GRAMS 386
17.3.3 BAG OF WORDS 386
17.3.4 VECTOR REPRESENTATIONS 387
17.3.5 DISTRIBUTED AND REDUCED REPRESENTATIONS 387
17.3.6 OTHER REPRESENTATIONS 391
17.3.7 IDENTIFYING AND ENHANCING THE VOCABULARY 391
17.4 THE CASE BASE CONTAINER 395
17.4.1 HYPERTEXT 395
17.4.2 INFORMATION EXTRACTION 396
CONTENTS XV
17.4.3 INFORMATION ENTITIES IN BASIC CASE RETRIEVAL NETS . 397
17.5 THE SIMILARITY CONTAINER 397
17.5.1 RELEVANCE-ORIENTED MEASURES 397
17.5.2 STRUCTURE-ORIENTED SIMILARITY MEASURES 398
17.5.3 MEASURES FOR SEGMENTS 400
17.5.4 TEXT TO TEXT SIMILARITY MEASURES 401
17.6 THE ADAPTATION CONTAINER 402
17.7 WHAT TEXTUAL CBR METHOD SHOULD I USE? 403
17.8 TOOLS 404
17.9 CHAPTER SUMMARY 405
17.10 BACKGROUND INFORMATION 405
17.11 EXERCISES 406
REFERENCES 407
18 IMAGES 411
18.1 ABOUT THIS CHAPTER 411
18.2 GENERAL ASPECTS 411
18.3 IMAGE STRUCTURE 413
18.3.1 IMAGE LEVELS 413
18.4 THE LEVEL STRUCTURE 415
18.5 THE IMAGE PIXEL LEVEL 417
18.5.1 THE GEOMETRIC LEVEL 418
18.5.2 THE SYMBOLIC AND DOMAIN-SPECIFIC LEVEL 421
18.5.3 THE OVERALL LEVEL 423
18.6 SEMANTICS 425
18.6.1 AESTHETICS 428
18.7 KNOWLEDGE CONTAINERS 431
18.7.1 THE VOCABULARY CONTAINER 431
18.7.2 THE SIMILARITY CONTAINER 431
18.7.3 THE CASE BASE CONTAINER 433
18.7.4 THE ADAPTATION CONTAINER 436
18.8 REVISION 436
18.9 STANDARDS 436
18.10 HOW TO DESIGN SUCH A CBR SYSTEM? 437
18.11 APPLICATIONS 437
18.12 TOOLS 438
18.13 CHAPTER SUMMARY 438
18.14 BACKGROUND INFORMATION 439
18.15 EXERCISES 440
REFERENCES 441
19 SENSOR DATA AND SPEECH 443
19.1 ABOUT THIS CHAPTER 443
19.2 GENERAL ASPECTS 443
19.3 THE LEVEL STRUCTURE 445
19.3.1 THE LOW LEVEL OF SIGNALS 446
XVI CONTENTS
19.3.2 THE FEATURE LEVEL 447
19.3.3 THE SYMBOLIC AND THE OVERALL LEVEL 448
19.4 SEMANTICS 449
19.5 REMARKS ON CBR PROCESS STEPS 449
19.5.1 CASES AND CASE BASES 449
19.5.2 SIMILARITY 450
19.5.3 RETRIEVAL 451
19.6 SPEECH RECOGNITION 451
19.6.1 THE WHOLE SPEECH PROCESS 452
19.6.2 THE ROLE OF THE LEVELS 453
19.6.3 STRUCTURE OF A SPEECH RECOGNITION SYSTEM 454
19.6.4 CASES AND CASE BASES 454
19.6.5 SPEECH FEATURE EXTRACTION AND SIMILARITY 454
19.6.6 THE WORD LEVEL AND THE VOCABULARY IN SPEECH 456
19.6.7 THE OVERALL LEVEL 456
19.6.8 SEMANTICS 457
19.6.9 ADAPTATION 457
19.6.10 ERRORS 458
19.7 APPLICATIONS 459
19.7.1 DIAGNOSIS 459
19.7.2 SPEECH APPLICATIONS 459
19.7.3 CONVERSATIONAL CBR 460
19.8 TOOLS 460
19.9 CHAPTER SUMMARY 461
19.10 BACKGROUND INFORMATION 461
19.11 EXERCISES 462
REFERENCES 463
20 CONVERSATIONAL CBR 465
20.1 ABOUT THIS CHAPTER 465
20.2 GENERAL ASPECTS 465
20.3 CONVERSATIONAL CBR 466
20.4 KNOWLEDGE CONTAINERS 467
20.4.1 THE VOCABULARY CONTAINER 467
20.4.2 THE CASE BASE CONTAINER 469
20.4.3 THE SIMILARITY CONTAINER 469
20.4.4 THE ADAPTATION CONTAINER 469
20.5 BASIC CONVERSATION SYSTEMS 470
20.5.1 PROCESSING THE INITIAL DESCRIPTION 470
20.5.2 DIALOGUE MANAGEMENT 470
20.5.3 DIALOGUE FORMALISMS 472
20.5.4 LENGTHS OF CONVERSATIONS 475
20.6 ARCHITECTURES FOR DIALOGUES 476
20.7 QUALITY AND EVALUATIONS OF DIALOGUES 478
20.8 MORE ON DIALOGUES 479
CONTENTS XVII
20.8.1 THE ROLE OF THESAURI AND ONTOLOGIES 479
20.8.2 IMAGES IN DIALOGUES 480
20.8.3 DIALOGUE CASE BASES 481
20.9 DOMAINS, APPLICATIONS, COMMERCIAL USE 481
20.10 TOOLS 482
20.11 CHAPTER SUMMARY 482
20.12 BACKGROUND INFORMATION 483
20.13 EXERCISES 484
REFERENCES 484
21 KNOWLEDGE MANAGEMENT 487
21.1 ABOUT THIS CHAPTER 487
21.2 GENERAL ASPECTS 487
21.3 KNOWLEDGE MANAGEMENT 488
21.3.1 KNOWLEDGE AND KNOWLEDGE MANAGEMENT 488
21.3.2 KNOWLEDGE AND DECISION MAKING 488
21.3.3 SOME KNOWLEDGE MANAGEMENT PROBLEMS 489
21.3.4 KNOWLEDGE MANAGEMENT: AN ORGANISATIONAL DISCIPLINE 489
21.3.5 KNOWLEDGE MANAGEMENT CYCLE 491
21.4 CASE-BASED REASONING AND KNOWLEDGE MANAGEMENT 491
21.5 CBR IMPLEMENTING KM CYCLES 493
21.5.1 KNOWLEDGE INFRASTRUCTURE AND ORGANISATION 493
21.5.2 KNOWLEDGE ORGANISATION AND RETRIEVAL 494
21.5.3 KNOWLEDGE RETRIEVAL AND REUSE 495
21.5.4 KNOWLEDGE SHARING 497
21.6 FOR WHICH KM TASKS SHOULD I USE CBR? 499
21.7 TOOLS 500
21.8 CHAPTER SUMMARY 501
21.9 BACKGROUND INFORMATION 501
21.10 EXERCISES 503
REFERENCES 503
PART V ADDITIONS
22 BASIC FORMAL DEFINITIONS AND METHODS 509
22.1 ABOUT THIS CHAPTER 509
22.2 GENERAL ASPECTS 509
22.3 CORRECTNESS AND LOGIC 510
22.3.1 PROPOSITIONAL LOGIC 510
22.3.2 PREDICATE LOGIC 511
22.3.3 CONSTRAINTS 514
22.3.4 RULES 514
22.3.5 REASONING IN LOGIC 515
22.4 INFORMATION THEORY AND ENTROPY 516
22.5 UTILITIES 518
22.5.1 OPTIMIZATION 519
XVIII CONTENTS
22.6 CHAPTER SUMMARY 520
22.7 BACKGROUND INFORMATION 520
REFERENCES 521
23 RELATIONS AND COMPARISONS WITH OTHER TECHNIQUES 523
23.1 ABOUT THIS CHAPTER 523
23.2 GENERAL ASPECTS 523
23.3 SYSTEMS WITH RETRIEVAL ENGINES 524
23.3.1 DATABASE MANAGEMENT SYSTEMS 524
23.3.2 INFORMATION RETRIEVAL SYSTEMS 525
23.3.3 PATTERN RECOGNITION SYSTEMS 527
23.3.4 KNOWLEDGE COMPARISON FOR RETRIEVAL SYSTEMS 529
23.4 EXPLICIT AND IMPLICIT KNOWLEDGE REPRESENTATION 530
23.4.1 KNOWLEDGE-BASED SYSTEMS 530
23.4.2 MACHINE LEARNING 531
23.5 INFLUENCE FACTORS 532
23.5.1 COGNITIVE SCIENCE 533
23.5.2 ANALOGICAL REASONING 534
23.5.3 UNCERTAINTY . . . 535
23.6 CHAPTER SUMMARY 537
23.7 BACKGROUND INFORMATION 537
REFERENCES 537
INDEX 539 |
| any_adam_object | 1 |
| author | Richter, Michael M. 1938-2020 Weber, Rosina O. |
| author_GND | (DE-588)120378620 (DE-588)173950051 |
| author_facet | Richter, Michael M. 1938-2020 Weber, Rosina O. |
| author_role | aut aut |
| author_sort | Richter, Michael M. 1938-2020 |
| author_variant | m m r mm mmr r o w ro row |
| building | Verbundindex |
| bvnumber | BV041460849 |
| classification_rvk | ST 130 ST 530 |
| ctrlnum | (OCoLC)864529746 (DE-599)DNB1038181771 |
| dewey-full | 006.333 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.333 |
| dewey-search | 006.333 |
| dewey-sort | 16.333 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV041460849 |
| illustrated | Illustrated |
| indexdate | 2024-09-10T01:03:08Z |
| institution | BVB |
| isbn | 9783642401664 9783642401671 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026907210 |
| oclc_num | 864529746 |
| open_access_boolean | |
| owner | DE-11 DE-634 DE-83 |
| owner_facet | DE-11 DE-634 DE-83 |
| physical | XVIII, 546 S. Ill., graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| spelling | Richter, Michael M. 1938-2020 Verfasser (DE-588)120378620 aut Case-based reasoning a textbook Michael M. Richter ; Rosina O. Weber Berlin [u.a.] Springer 2013 XVIII, 546 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Fallbasiertes Schließen (DE-588)4363288-9 gnd rswk-swf Fallbasiertes Schließen (DE-588)4363288-9 s DE-604 Weber, Rosina O. Verfasser (DE-588)173950051 aut X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4388498&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026907210&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Richter, Michael M. 1938-2020 Weber, Rosina O. Case-based reasoning a textbook Fallbasiertes Schließen (DE-588)4363288-9 gnd |
| subject_GND | (DE-588)4363288-9 |
| title | Case-based reasoning a textbook |
| title_auth | Case-based reasoning a textbook |
| title_exact_search | Case-based reasoning a textbook |
| title_full | Case-based reasoning a textbook Michael M. Richter ; Rosina O. Weber |
| title_fullStr | Case-based reasoning a textbook Michael M. Richter ; Rosina O. Weber |
| title_full_unstemmed | Case-based reasoning a textbook Michael M. Richter ; Rosina O. Weber |
| title_short | Case-based reasoning |
| title_sort | case based reasoning a textbook |
| title_sub | a textbook |
| topic | Fallbasiertes Schließen (DE-588)4363288-9 gnd |
| topic_facet | Fallbasiertes Schließen |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4388498&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026907210&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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