Emerging paradigms in machine learning:
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| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg [u.a.]
Springer
2013
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| Series: | Smart innovation, systems and technologies
13 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XXII, 495 S. Ill., graph. Darst. |
| ISBN: | 9783642286988 |
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| 245 | 1 | 0 | |a Emerging paradigms in machine learning |c Sheela Ramanna ... (eds.) |
| 264 | 1 | |a Berlin ; Heidelberg [u.a.] |b Springer |c 2013 | |
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IMAGE 1
CONTENTS
1 EMERGING PARADIGMS IN MACHINE LEARNING: AN INTRODUCTION 1 SHEELA
RAMANNA, LAKHMI C. JAIN, ROBERT J. HOWLETT 1.1 INTRODUCTION 1
1.2 CHAPTERS OF THE BOOK 4
1.3 CONCLUDING REMARKS 7
REFERENCES AND FURTHER READINGS 7
PART A: FOUNDATIONS
2 EXTENSIONS OF DYNAMIC PROGRAMMING AS A NEW TOOL FOR DECISION TREE
OPTIMIZATION 11
ABDULAZIZ ALKHALID, IGOR CHIKALOV, SHAHID HUSSAIN, MIKHAIL MOSHKOV 2.1
INTRODUCTION 11
2.2 BASIC NOTIONS 12
2.2.1 DECISION TABLES AND TREES 13
2.2.2 COST FUNCTIONS 14
2.3 REPRESENTATION OF SETS OF A-DECISION TREES AND DECISION TREES . . .
15 2.4 OPTIMIZATION OF A-DECISION TREES 18
2.4.1 PROPER SUBGRAPHS OF GRAPH A A ( T ) 18
2.4.2 PROCEDURE OF OPTIMIZATION 18
2.4.3 POSSIBILITIES OF SEQUENTIAL OPTIMIZATION 20
2.4.4 EXPERIMENTAL RESULTS 21
2.5 RELATIONSHIPS BETWEEN DEPTH AND NUMBER OF MISCLASSIFICATIONS. 24
2.5.1 COMPUTING THE RELATIONSHIPS 24
2.5.2 EXPERIMENTAL RESULTS 26
2.6 CONCLUSIONS 27
REFERENCES 28
HTTP://D-NB.INFO/1019655267
IMAGE 2
XII
CONTENTS
3 OPTIMISED INFORMATION ABSTRACTION IN GRANULAR MIN/MAX CLUSTERING 31
ANDRZEJ BARGIELA, WITOLD PEDRYCZ 3.1 INTRODUCTORY COMMENTS 31
3.2 GRANULAR INFORMATION IN SYSTEMS MODELING 35
3.3 INFORMATION DENSITY BASED GRANULATION 36
3.4 GRANULAR REPRESENTATIVES O F DATA 40
3.5 GRANULAR REFINEMENT OF PROTOTYPES 44
3.6 CONCLUSIONS 47
REFERENCES 47
4 MINING INCOMPLETE DATA-A ROUGH SET APPROACH 49
JERZY W. GRZYMALA-BUSSE, ZDZISLAW S. HIPPE 4.1 INTRODUCTION 49
4.2 BLOCKS OF ATTRIBUTE-VALUE PAIRS 51
4.3 APPROXIMATIONS 54
4.4 TWO ALGORITHMS 57
4.5 GLOBAL MLEM2 62
4.6 LOCAL MLEM2 63
4.7 INCOMPLETE DATA SETS WITH NUMERICAL ATTRIBUTES 66
4.8 EXPERIMENTS 70
4.9 CONCLUSIONS 71
REFERENCES 72
5 ROLES PLAYED BY BAYESIAN NETWORKS IN MACHINE LEARNING: AN EMPIRICAL
INVESTIGATION 75
ESTEVAM R. HRUSCHKA JR., MARIA DO CARMO NICOLETTI 5.1 INTRODUCTION 75
5.2 RELEVANT CONCEPTS RELATED TO BAYESIAN NETWORKS AND BAYESIAN
CLASSIFIERS 76
5.3 LEARNING BAYESIAN NETWORKS AND BAYESIAN CLASSIFIERS FROM DATA 81
5.3.1 THE NAIVE BAYES CLASSIFIER 81
5.3.2 THE PC ALGORITHM 82
5.3.3 THE K2 ALGORITHM 84
5.4 BAYESIAN CLASSIFIERS IN FEATURE SUBSET SELECTION 85
5.4.1 CONSIDERATIONS ABOUT THE FEATURE SUBSET SELECTION (FSS) PROBLEM 85
5.4.2 FEATURE SUBSET SELECTION BY BAYESIAN NETWORKS - THE K2X2 METHOD 89
5.5 BAYESIAN CLASSIFIERS IN IMPUTATION PROCESSES 94
5.5.1 CONSIDERATIONS ABOUT IMPUTATION PROCESSES 94
5.5.2 COMMONLY USED IMPUTATION METHODS 95
5.5.3 IMPUTATION BY BAYESIAN NETWORKS AND THE K2I%2 METHOD 96
IMAGE 3
CONTENTS XIII
5.6 POST-PROCESSING A BAYESIAN CLASSIFIER INTO A SET OF RULES 97
5.6.1 TRANSLATING A BAYESIAN CLASSIFIER INTO A REDUCED SET OF RULES -
THE BAYESRULE ALGORITHM 98
5.6.2 USING BAYESRULE - EXPERIMENTS AND RESULTS 103
5.7 CONCLUSION 110
REFERENCES I L L
6 EVOLVING INTELLIGENT SYSTEMS: METHODS, ALGORITHMS AND APPLICATIONS 117
ANDRE LEMOS, WALMIR CAMINHAS, FERNANDO GOMIDE 6.1 INTRODUCTION 117
6.2 EVOLVING FUZZY SYSTEMS 120
6.2.1 EVOLVING TAKAGI-SUGENO (ETS) 120
6.2.2 OTHER EVOLVING FUZZY MODELS 124
6.3 EVOLVING MULTIVARIATE GAUSSIAN 127
6.3.1 GAUSSIAN PARTICIPATORY EVOLVING CLUSTERING 128
6.3.2 EVOLVING MULTIVARIATE GAUSSIAN FUZZY MODEL 134
6.4 EVOLVING FUZZY LINEAR REGRESSION TREES 136
6.4.1 FUZZY LINEAR REGRESSION TREES 137
6.4.2 INCREMENTAL LEARNING ALGORITHM 141
6.5 EXPERIMENTS 146
6.5.1 SHORT TERM ELECTRICITY LOAD FORECASTING 147
6.5.2 TREE RINGS 152
6.6 CONCLUSION 155
REFERENCES 156
7 EMERGING TRENDS IN MACHINE LEARNING: CLASSIFICATION OF STOCHASTICALLY
EPISODIC EVENTS 161
B. JOHN OOMMEN, COLIN BELLINGER 7.1 INTRODUCTION 162
7.1.1 PROBLEM FORMULATION 162
7.1.2 SE EVENT RECOGNITION 163
7.1.3 CHARACTERISTICS OF THE DOMAIN OF PROBLEMS 164
7.1.4 OVERVIEW OF OUR SOLUTION 165
7.2 PATTERN RECOGNITION: STATE OF THE ART 166
7.2.1 SUPERVISED LEARNING 166
7.2.2 ALTERNATIVE LEARNING PARADIGMS 168
7.2.3 SAMPLING 169
7.2.4 DYNAMIC CLASSIFICATION 169
7.3 MODELLING THE PROBLEM 170
7.3.1 APPLICATION DOMAIN 170
7.3.2 PROCURING DATA: ASPECTS OF SIMULATION 170
7.3.3 GENERATED DATASETS 173
7.4 PR SOLUTIONS 173
7.4.1 CLASSIFICATION SCENARIOS 174
IMAGE 4
XIV
CONTENTS
7.4.2 CLASSIFICATION 175
7.4.3 CLASSIFIER ASSESSMENT CRITERIA 176
7.5 RESULTS: SCENARIO 1 176
7.5.1 GENERAL PERFORMANCE 176
7.5.2 PERFORMANCE ON SHORT-AND LONG-RANGE DETONATIONS 179 7.5.3
PERFORMANCE AS A FUNCTION OF DISTANCE 180
7.5.4 EXPANDED FEATURE-SPACE 183
7.6 RESULTS: SCENARIO 2 186
7.6.1 GENERAL PERFORMANCE 186
7.6.2 PERFORMANCE ON SHORT-AND LONG-RANGE DETONATIONS 187 7.6.3
PERFORMANCE AS A FUNCTION OF DISTANCE 188
7.6.4 EXPANDED FEATURE-SPACE 190
7.7 DISCUSSION 191
7.7.1 RESULTS: SI 191
7.7.2 RESULTS: S2 192
7.8 CONCLUSIONS 193
REFERENCES 194
8 LEARNING O F DEFAULTS BY AGENTS IN A DISTRIBUTED MULTI-AGENT SYSTEM
ENVIRONMENT 197
HENRYK RYBINSKI, DOMINIK RYZKO, PRZEMYSLAW WIGCH 8.1 INTRODUCTION 197
8.2 RELATED WORK 198
8.3 MOTIVATION 200
8.4 PRELIMINARIES 204
8.4.1 DEFAULT LOGIC 204
8.4.2 DISTRIBUTED DEFAULT LOGIC 205
8.4.3 INDUCTIVE LOGIC PROGRAMMING 206
8.5 LEARNING DDL THEORY BY MAS 208
8.6 CONCLUSIONS 212
REFERENCES 212
9 ROUGH NON-DETERMINISTIC INFORMATION ANALYSIS: FOUNDATIONS AND ITS
PERSPECTIVE IN MACHINE LEARNING 215
HIROSHI SAKAI, HITOMI OKUMA, MICHINORI NAKATA 9.1 INTRODUCTION 215
9.2 FOUNDATIONS OF ROUGH SETS IN DISS 216
9.2.1 SOME DEFINITIONS AND ASPECTS IN DISS 216
9.2.2 MANIPULATION ALGORITHMS FOR EQUIVALENCE RELATIONS AND DATA
DEPENDENCY 220
9.3 FOUNDATIONS OF ROUGH NON-DETERMINISTIC INFORMATION ANALYSIS . . .
221 9.3.1 SOME DEFINITIONS AND ASPECTS IN NISS 221
9.3.2 A BASIC CHART AND TWO MODALITIES 222
9.3.3 COMPUTATIONAL COMPLEXITY IN NISS 223
9.3.4 POSSIBLE EQUIVALENCE CLASSES IN NISS 223
IMAGE 5
CONTENTS XV
9.3.5 SOME EXTENDED ASPECTS TO NISS 224
9.4 AN ASPECT OF QUESTION-ANSWERING AND DECISION MAKING IN NISS 229
9.5 RULE GENERATION IN NISS 230
9.5.1 RULE GENERATION TASKS IN A NIS 230
9.5.2 STABILITY FACTOR OF RULES IN THE UPPER SYSTEM 232
9.5.3 CURRENT STATE OF A RULE GENERATOR IN PROLOG 232
9.5.4 AN EXAMPLE OF EXECUTION BY A RULE GENERATOR 232
9.5".5 AN APPLICATION TO OTHER TYPES OF RULE GENERATION 235 9.6
PERSPECTIVE OF RNIA IN MACHINE LEARNING 238
9.6.1 HANDLING OF INEXACT DATA 238
9.6.2 LEARNING A DIS FROM A NIS BY CONSTRAINTS 239
9.6.3 TABLE DATA AND LOGICAL DATA IN MACHINE LEARNING 240 9.7 CONCLUDING
REMARKS 241
REFERENCES 241
10 INTRODUCTION TO PERCEPTION BASED COMPUTING 249
ANDRZEJ SKOWRON, PIOTR WASILEWSKI 10.1 INTRODUCTION 249
10.2 MOTIVATION FOR PERCEPTION BASED COMPUTING 251
10.3 PERCEPTION [15, 3] 253
10.4 INTERACTIVE INFORMATION SYSTEMS 255
10.5 INTERACTIVE COMPUTING 259
10.6 ACTION ATTRIBUTES AND PLANS 261
10.7 TOWARDS GRANULE SEMANTICS 265
10.8 CONCLUSIONS 270
REFERENCES 271
11 OVERLAPPING, RARE EXAMPLES AND CLASS DECOMPOSITION IN LEARNING
CLASSIFIERS FROM IMBALANCED DATA 277
JERZY STEFANOWSKI 11.1 INTRODUCTION 278
11.2 EVALUATION MEASURES FOR LEARNING CLASSIFIERS FROM IMBALANCED DATA
280
11.3 EARLIER STUDIES WITH DATA FACTORS IN CLASS IMBALANCE 281
11.4 GENERATION OF NEW ARTIFICIAL DATA SETS 285
11.5 EXPERIMENTAL ANALYSIS OF INFLUENCE OF CRITICAL FACTORS ON
CLASSIFIERS 289
11.6 IMPROVING CLASSIFIERS BY FOCUSED RE-SAMPLING METHODS 293 11.6.1
INFORMED UNDERSAMPLING 294
11.6.2 INFORMED OVERSAMPLING METHODS 295
11.6.3 SPIDER METHOD 295
11.7 EXPERIMENTS WITH FOCUSED RE-SAMPLING METHODS 297
11.8 FINAL REMARKS 301
REFERENCES 302
IMAGE 6
XVI CONTENTS
12 A GRANULAR COMPUTING PARADIGM FOR CONCEPT LEARNING 307 YIYU YAO,
XIAOFEI DENG 12.1 INTRODUCTION 307
12.2 A TRIARCHIC THEORY OF GRANULAR COMPUTING 308
12.2.1 MULTILEVEL, MULTIVIEW GRANULAR STRUCTURES 309
12.2.2 PHILOSOPHY: STRUCTURED THINKING 312
12.2.3 METHODOLOGY: STRUCTURED PROBLEM SOLVING 313
12.2.4 COMPUTATION: STRUCTURED INFORMATION PROCESSING 314 12.3 GRANULAR
COMPUTING AND CONCEPT LEARNING 315
12.3.1 GRANULES AND CONCEPTS 315
12.3.2 GRANULATION AND CLASSIFICATION 316
12.3.3 CONCEPT LEARNING AS SEARCHING 318
12.4 A MODEL FOR LEARNING A CLASSIFICATION 319
12.4.1 A DECISION LOGIC LANGUAGE IN AN INFORMATION TABLE 320 12.4.2
CONJUNCTIVELY DEFINABLE CONCEPTS 321
12.4.3 ATTRIBUTE-ORIENTED SEARCH STRATEGIES IN A SPACE OF PARTITIONS
DEFINED BY SUBSETS OF ATTRIBUTES 321
12.4.4 ATTRIBUTE-VALUE-ORIENTED SEARCH STRATEGIES IN A SPACE OF
COVERINGS DEFINED BY FAMILIES OF SETS OF ATTRIBUTE-VALUE PAIRS 323
12.5 CONCLUSION 324
REFERENCES 325
PART B: APPLICATIONS
13 IDENTIFYING CALENDAR-BASED PERIODIC PATTERNS 329
JHIMLI ADHIKARI, P.R. RAO 13.1 INTRODUCTION 329
13.2 RELATED WORK 332
13.3 CALENDAR-BASED PERIODIC PATTERNS 333
13.3.1 EXTENDING CERTAINTY FACTOR 334
13.3.2 EXTENDING CERTAINTY FACTOR WITH RESPECT TO OTHER INTERVALS 337
13.4 MINING CALENDAR-BASED PERIODIC PATTERNS 339
13.4.1 IMPROVING MINING CALENDAR-BASED PERIODIC PATTERNS 339 13.4.2 DATA
STRUCTURE 339
13.4.2 A MODIFIED ALGORITHM 341
13.5 EXPERIMENTAL STUDIES 344
13.5.1 SELECTION O F MININTERVAL AND MAXGAP 348
13.5.2 SELECTION OF MINSUPP 351
13.5.3 PERFORMANCE ANALYSIS 352
13.6 CONCLUSIONS 355
REFERENCES 356
IMAGE 7
CONTENTS XVII
14 THE MAMDANI EXPERT-SYSTEM WITH PARAMETRIC FAMILIES O F FUZZY
CONSTRAINTS IN EVALUATION OF CANCER PATIENT SURVIVAL LENGTH 359
ELISABETH RAKUS-ANDERSSON 14.1 INTRODUCTION 359
14.2 MAKING FUZZIFICATION OF INPUT AND OUTPUT VARIABLE ENTRIES BY
PARAMETRIC S-FUNCTIONS 361
14.3 THE RULE BASED PROCESSING PART OF SURVIVING LENGTH MODEL 370 14.4
DEFUZZIFICATION OF THE OUTPUT VARIABLE 372
14.5 THE SURVIVAL LENGTH PROGNOSIS FOR A SELECTED PATIENT 372
14.6 CONCLUSIONS 376
REFERENCES 377
15 SUPPORT VECTOR MACHINES IN BIOMEDICAL AND BIOMETRICAL APPLICATIONS
379
KRZYSZTOF A. CYRAN, JOLANTA KAWULOK, MICHAL KAWULOK, MAGDALENA STAWARZ,
MARCIN MICHALAK, MONIKA PIETROWSKA, PIOTR WIDLAK, JOANNA POLANSKA 15.1
INTRODUCTION 380
15.2 SUPPORT VECTOR MACHINES APPLIED IN THE CLASSIFICATION OF MASS
SPECTRA 384
15.2.1 MS SPECTRA PREPROCESSING 384
15.2.2 PREPARING SPECTRA TO CLASSIFICATION 388
15.2.3 CLASSIFICATION 389
15.3 SUPPORT VECTOR MACHINES APPLIED TO HUMAN FACE RECOGNITION . . . 396
15.3.1 FACE RECOGNITION PROCESS 397
15.3.2 EVALUATION PROTOCOL 398
15.3.3 SELECTING SVM TRAINING SET 399
15.3.4 FACE DETECTION 402
15.3.5 FEATURE VECTORS COMPARISON 407
15.3.6 MULTI-METHOD FUSION 411
15.4 CONCLUSIONS 413
REFERENCES 413
16 WORKLOAD MODELING FOR MULTIMEDIA SURVEILLANCE SYSTEMS 419 MUKESH
SAINI, PRADEEP K. ATREY, MOHAN S. KANKANHALLI 16.1 INTRODUCTION 419
16.1.1 ISSUES IN WORKLOAD CHARACTERIZATION 421
16.1.2 CONTRIBUTIONS SUMMARY 421
16.1.3 CHAPTER ORGANIZATION 422
16.2 SURVEILLANCE SYSTEM 422
16.3 PREVIOUS WORK 423
16.4 PROPOSED MODEL 424
16.4.1 TARGET FLOW GRAPH (TFG) 425
16.4.2 MARKOV CHAIN CONSTRUCTION 426
16.4.3 TASK ARRIVAL 428
IMAGE 8
XVIII CONTENTS
16.4.4 PROCESSING DEMAND 429
16.4.5 MEMORY DEMAND 430
16.5 PERFORMANCE EVALUATION 430
16.5.1 SYSTEM RESPONSE TIME 431
16.5.2 FRAME DROP PROBABILITY 432
16.6 EXPERIMENTS 433
16.6.1 IMPLEMENTATION 433
16.6.2 HYPOTHESIS TESTING: NORMAL DISTRIBUTED PROCESSING TIME 435
16.6.3 RESPONSE TIME 435
16.6.4 FRAME DROP PROBABILITY 437
16.6.5 IMPLICATIONS 437
16.7 CONCLUSIONS AND FUTURE WORK 438
REFERENCES 439
17 ROUGH SET AND ARTIFICIAL NEURAL NETWORK APPROACH TO COMPUTATIONAL
STYLISTICS 441
URSZULA STANCZYK 17.1 INTRODUCTION 441
17.2 BASICS OF COMPUTATIONAL STYLISTICS 442
17.2.1 OBJECTIVES OF TEXTUAL ANALYSIS 443
17.2.2 SHORT HISTORICAL OVERVIEW 444
17.2.3 METHODOLOGIES EMPLOYED 445
17.3 CONNNECTIONIST AND RULE-BASED CLASSIFICATION 447
17.3.1 ARTIFICIAL NEURAL NETWORKS 447
17.3.2 ROUGH SET THEORY 449
17.4 EXPERIMENTAL SETUP 452
17.4.1 INPUT DATASETS 452
17.4.2 CONNECTIONIST CLASSIFICATION 455
17.4.3 RULE-BASED CLASSIFICATION 455
17.4.4 ANALYSIS OF CHARACTERISTIC FEATURES 458
17.4.5 PERFORMANCE FOR FEATURE REDUCTION 462
17.5 CONCLUSIONS AND FUTURE RESEARCH 468
REFERENCES 469
18 APPLICATION OF LEARNING ALGORITHMS TO IMAGE SPAM EVOLUTION 471 SHRUTI
WAKADE, KATHY J. LISZKA, CHIEN-CHUNG CHAN 18.1 INTRODUCTION 471
18.2 RELATED WORK 473
18.3 SPAM IMAGES EVOLUTION AND DATASETS 474
18.3.1 TYPES AND TRENDS OF IMAGE SPAM 474
18.3.2 THE CORPUS 477
IMAGE 9
CONTENTS XIX
18.4 LEARNING FROM SPAM IMAGES 479
18.4.1 SPAM IMAGE REPRESENTATION 479
18.5 EXPERIMENTS 481
18.5.1 EXPERIMENT WITH J48 482
18.5.2 EXPERIMENT WITH REPTREE 482
18.6 VALIDATION BY FEATURE ANALYSIS 486
18.7 CONCLUSIONS 492
REFERENCES 493
AUTHOR INDEX 497 |
| any_adam_object | 1 |
| author2 | Ramanna, Sheela |
| author2_role | edt |
| author2_variant | s r sr |
| author_facet | Ramanna, Sheela |
| building | Verbundindex |
| bvnumber | BV040455898 |
| classification_rvk | ST 300 ST 302 |
| ctrlnum | (OCoLC)812375768 (DE-599)DNB1019655267 |
| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| genre_facet | Aufsatzsammlung |
| id | DE-604.BV040455898 |
| illustrated | Illustrated |
| indexdate | 2024-08-21T00:13:09Z |
| institution | BVB |
| isbn | 9783642286988 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025303439 |
| oclc_num | 812375768 |
| open_access_boolean | |
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| owner_facet | DE-83 DE-11 DE-473 DE-BY-UBG |
| physical | XXII, 495 S. Ill., graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| series | Smart innovation, systems and technologies |
| series2 | Smart innovation, systems and technologies |
| spelling | Emerging paradigms in machine learning Sheela Ramanna ... (eds.) Berlin ; Heidelberg [u.a.] Springer 2013 XXII, 495 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Smart innovation, systems and technologies 13 Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Maschinelles Lernen (DE-588)4193754-5 s DE-604 Ramanna, Sheela edt Erscheint auch als Online-Ausgabe 978-3-642-28699-5 Smart innovation, systems and technologies 13 (DE-604)BV036867859 13 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3976355&prov=M&dok%5Fvar=1&dok%5Fext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025303439&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Emerging paradigms in machine learning Smart innovation, systems and technologies Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4143413-4 |
| title | Emerging paradigms in machine learning |
| title_auth | Emerging paradigms in machine learning |
| title_exact_search | Emerging paradigms in machine learning |
| title_full | Emerging paradigms in machine learning Sheela Ramanna ... (eds.) |
| title_fullStr | Emerging paradigms in machine learning Sheela Ramanna ... (eds.) |
| title_full_unstemmed | Emerging paradigms in machine learning Sheela Ramanna ... (eds.) |
| title_short | Emerging paradigms in machine learning |
| title_sort | emerging paradigms in machine learning |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen Aufsatzsammlung |
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