Combinatorial machine learning: a rough set approach
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2011
|
| Schriftenreihe: | Studies in computational intelligence
360 |
| Schlagwörter: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=3710632&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=024178079&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Literaturangaben |
| Umfang: | XIV, 181 S. graph. Darst. |
| ISBN: | 9783642209949 3642209947 |
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Datensatz im Suchindex
| _version_ | 1819324831596806144 |
|---|---|
| adam_text | IMAGE 1
CONTENTS
INTRODUCTION 1
1 EXAMPLES FROM APPLICATIONS 5
1.1 PROBLEMS 5
1.2 DECISION TABLES 7
1.3 EXAMPLES 9
1.3.1 THREE CUPS AND SMALL BALL 9
1.3.2 DIAGNOSIS OF ONE-GATE CIRCUIT 10
1.3.3 PROBLEM OF THREE POST-OFFICES 13
1.3.4 RECOGNITION OF DIGITS 15
1.3.5 TRAVELING SALESMAN PROBLEM WITH FOUR CITIES 16 1.3.6 TRAVELING
SALESMAN PROBLEM WITH N 4 CITIES 18 1.3.7 DATA TABLE WITH EXPERIMENTAL
DATA 19
1.4 CONCLUSIONS 20
PART I TOOLS
2 SETS OF TESTS, DECISION RULES AND TREES 23
2.1 DECISION TABLES, TREES, RULES AND TESTS 23
2.2 SETS OF TESTS, DECISION RULES AND TREES 25
2.2.1 MONOTONE BOOLEAN FUNCTIONS 25
2.2.2 SET OF TESTS 26
2.2.3 SET OF DECISION RULES 29
2.2.4 SET OF DECISION TREES 32
2.3 RELATIONSHIPS AMONG DECISION TREES, RULES AND TESTS 34 2.4
CONCLUSIONS 36
3 BOUNDS ON COMPLEXITY OF TESTS, DECISION RULES AND TREES 37
3.1 LOWER BOUNDS 37
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1010922459
DIGITALISIERT DURCH
IMAGE 2
XII CONTENTS
3.2 UPPER BOUNDS 43
3.3 CONCLUSIONS 46
4 ALGORITHMS FOR CONSTRUCTION OF TESTS, DECISION RULES AND TREES 47
4.1 APPROXIMATE ALGORITHMS FOR OPTIMIZATION OF TESTS AND DECISION RULES
47
4.1.1 SET COVER PROBLEM 48
4.1.2 TESTS: FROM DECISION TABLE TO SET COVER PROBLEM . .. 50 4.1.3
DECISION RULES: FROM DECISION TABLE TO SET COVER PROBLEM 50
4.1.4 FROM SET COVER PROBLEM TO DECISION TABLE 52
4.2 APPROXIMATE ALGORITHM FOR DECISION TREE OPTIMIZATION . . .. 55 4.3
EXACT ALGORITHMS FOR OPTIMIZATION OF TREES, RULES AND TESTS 59
4.3.1 OPTIMIZATION OF DECISION TREES 59
4.3.2 OPTIMIZATION OF DECISION RULES 61
4.3.3 OPTIMIZATION OF TESTS 64
4.4 CONCLUSIONS 67
5 DECISION TABLES WITH MANY-VALUED DECISIONS 69
5.1 EXAMPLES CONNECTED WITH APPLICATIONS 69
5.2 MAIN NOTIONS 72
5.3 RELATIONSHIPS AMONG DECISION TREES, RULES AND TESTS 74 5.4 LOWER
BOUNDS 76
5.5 UPPER BOUNDS 77
5.6 APPROXIMATE ALGORITHMS FOR OPTIMIZATION OF TESTS AND DECISION RULES
78
5.6.1 OPTIMIZATION OF TESTS 78
5.6.2 OPTIMIZATION OF DECISION RULES 79
5.7 APPROXIMATE ALGORITHMS FOR DECISION TREE OPTIMIZATION . .. 81 5.8
EXACT ALGORITHMS FOR OPTIMIZATION OF TREES, RULES AND TESTS 83
5.9 EXAMPLE 83
5.10 CONCLUSIONS 86
6 APPROXIMATE TESTS, DECISION TREES AND RULES 87
6.1 MAIN NOTIONS 87
6.2 RELATIONSHIPS AMONG A-TREES, A-RULES AND A-TESTS 89
6.3 LOWER BOUNDS 91
6.4 UPPER BOUNDS , 96
6.5 APPROXIMATE ALGORITHM FOR A-DECISION RULE OPTIMIZATION 100
6.6 APPROXIMATE ALGORITHM FOR A-DECISION TREE OPTIMIZATION 103
IMAGE 3
CONTENTS XIII
6.7 ALGORITHMS FOR A-TEST OPTIMIZATION 106
6.8 EXACT ALGORITHMS FOR OPTIMIZATION OF A-DECISION TREES AND RULES 106
6.9 CONCLUSIONS 108
PART II APPLICATIONS
7 SUPERVISED LEARNING 113
7.1 CLASSIFIERS BASED ON DECISION TREES 114
7.2 CLASSIFIERS BASED ON DECISION RULES 115
7.2.1 USE OF GREEDY ALGORITHMS 115
7.2.2 USE OF DYNAMIC PROGRAMMING APPROACH 116 7.2.3 FROM TEST TO
COMPLETE SYSTEM OF DECISION RULES 116 7.2.4 FROM DECISION TREE TO
COMPLETE SYSTEM OF DECISION RULES 117
7.2.5 SIMPLIFICATION OF RULE SYSTEM 117
7.2.6 SYSTEM OF RULES AS CLASSIFIER 118
7.2.7 PRUNING 118
7.3 LAZY LEARNING ALGORITHMS 119
7.3.1 FC-NEAREST NEIGHBOR ALGORITHM 120
7.3.2 LAZY DECISION TREES AND RULES 120
7.3.3 LAZY LEARNING ALGORITHM BASED ON DECISION RULES... 122 7.3.4 LAZY
LEARNING ALGORITHM BASED ON REDUCTS 124 7.4 CONCLUSIONS 125
8 LOCAL AND GLOBAL APPROACHES TO STUDY OF TREES AND RULES 127
8.1 BASIC NOTIONS 127
8.2 LOCAL APPROACH TO STUDY OF DECISION TREES AND RULES 129 8.2.1 LOCAL
SHANNON FUNCTIONS FOR ARBITRARY INFORMATION SYSTEMS 130
8.2.2 RESTRICTED BINARY INFORMATION SYSTEMS 132
8.2.3 LOCAL SHANNON FUNCTIONS FOR FINITE INFORMATION SYSTEMS 135
8.3 GLOBAL APPROACH TO STUDY OF DECISION TREES AND RULES 136 8.3.1
INFINITE INFORMATION SYSTEMS 136
8.3.2 GLOBAL SHANNON FUNCTION /IF, FOR TWO-VALUED FINITE INFORMATION
SYSTEMS 140
8.4 CONCLUSIONS 141
9 DECISION TREES AND RULES OVER QUASILINEAR INFORMATION SYSTEMS 143
9.1 BOUNDS ON COMPLEXITY OF DECISION TREES AND RULES 144 9.1.1
QUASILINEAR INFORMATION SYSTEMS 144
IMAGE 4
XIV CONTENTS
9.1.2 LINEAR INFORMATION SYSTEMS 145
9.2 OPTIMIZATION PROBLEMS OVER QUASILINEAR INFORMATION SYSTEMS 147
9.2.1 SOME DEFINITIONS 148
9.2.2 PROBLEMS OF UNCONDITIONAL OPTIMIZATION 148
9.2.3 PROBLEMS OF UNCONDITIONAL OPTIMIZATION OF ABSOLUTE VALUES 149
9.2.4 PROBLEMS OF CONDITIONAL OPTIMIZATION 150
9.3 ON DEPTH OF ACYCLIC PROGRAMS 151
9.3.1 MAIN DEFINITIONS 151
9.3.2 RELATIONSHIPS BETWEEN DEPTH OF DETERMINISTIC AND NONDETERMINISTIC
ACYCLIC PROGRAMS 152
9.4 CONCLUSIONS 153
10 RECOGNITION OF WORDS AND DIAGNOSIS OF FAULTS 155
10.1 REGULAR LANGUAGE WORD RECOGNITION 155
10.1.1 PROBLEM OF RECOGNITION OF WORDS 155
10.1.2 A-SOURCES 156
10.1.3 TYPES OF REDUCED A-SOURCES 157
10.1.4 MAIN RESULT 158
10.1.5 EXAMPLES 159
10.2 DIAGNOSIS OF CONSTANT FAULTS IN CIRCUITS 161
10.2.1 BASIC NOTIONS 161
10.2.2 COMPLEXITY OF DECISION TREES FOR DIAGNOSIS OF FAULTS 164
10.2.3 COMPLEXITY OF CONSTRUCTION OF DECISION TREES FOR DIAGNOSIS 166
10.2.4 DIAGNOSIS OF ITERATION-FREE CIRCUITS 166
10.2.5 APPROACH TO CIRCUIT CONSTRUCTION AND DIAGNOSIS . . .. 169 10.3
CONCLUSIONS 169
FINAL REMARKS 171
REFERENCES 173
INDEX 179
|
| any_adam_object | 1 |
| author | Moshkov, Mikhail Ju Zielosko, Beata |
| author_facet | Moshkov, Mikhail Ju Zielosko, Beata |
| author_role | aut aut |
| author_sort | Moshkov, Mikhail Ju |
| author_variant | m j m mj mjm b z bz |
| building | Verbundindex |
| bvnumber | BV039160573 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)725083580 (DE-599)DNB1010922459 |
| 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 Mathematik Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Book |
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| id | DE-604.BV039160573 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T15:51:27Z |
| institution | BVB |
| isbn | 9783642209949 3642209947 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024178079 |
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| physical | XIV, 181 S. graph. Darst. |
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| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spellingShingle | Moshkov, Mikhail Ju Zielosko, Beata Combinatorial machine learning a rough set approach Studies in computational intelligence Maschinelles Lernen (DE-588)4193754-5 gnd Data Mining (DE-588)4428654-5 gnd Optimierungsproblem (DE-588)4390818-4 gnd Grobmenge (DE-588)4362502-2 gnd Entscheidungsregel (DE-588)4152406-8 gnd Entscheidungsbaum (DE-588)4347788-4 gnd Komplexitätstheorie (DE-588)4120591-1 gnd Wissensextraktion (DE-588)4546354-2 gnd Entscheidungstabelle (DE-588)4152411-1 gnd |
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| title | Combinatorial machine learning a rough set approach |
| title_auth | Combinatorial machine learning a rough set approach |
| title_exact_search | Combinatorial machine learning a rough set approach |
| title_full | Combinatorial machine learning a rough set approach Mikhail Moshkov and Beata Zielosko |
| title_fullStr | Combinatorial machine learning a rough set approach Mikhail Moshkov and Beata Zielosko |
| title_full_unstemmed | Combinatorial machine learning a rough set approach Mikhail Moshkov and Beata Zielosko |
| title_short | Combinatorial machine learning |
| title_sort | combinatorial machine learning a rough set approach |
| title_sub | a rough set approach |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Data Mining (DE-588)4428654-5 gnd Optimierungsproblem (DE-588)4390818-4 gnd Grobmenge (DE-588)4362502-2 gnd Entscheidungsregel (DE-588)4152406-8 gnd Entscheidungsbaum (DE-588)4347788-4 gnd Komplexitätstheorie (DE-588)4120591-1 gnd Wissensextraktion (DE-588)4546354-2 gnd Entscheidungstabelle (DE-588)4152411-1 gnd |
| topic_facet | Maschinelles Lernen Data Mining Optimierungsproblem Grobmenge Entscheidungsregel Entscheidungsbaum Komplexitätstheorie Wissensextraktion Entscheidungstabelle |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3710632&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=024178079&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT moshkovmikhailju combinatorialmachinelearningaroughsetapproach AT zieloskobeata combinatorialmachinelearningaroughsetapproach |