An introduction to computational learning theory:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. [u.a.]
MIT Press
1994
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XII, 207 S. graph. Darst. |
| ISBN: | 0262111934 9780262111935 |
Internformat
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Datensatz im Suchindex
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| adam_text |
AN INTRODUCTION TO COMPUTATIONAL LEARNING THEORY MICHAEL J. KEARNS UMESH
V. VAZIRANI THE MIT PRESS CAMBRIDGE, MASSACHUSETTS LONDON, ENGLAND
CONTENTS PREFACE XI 1 THE PROBABLY APPROXIMATELY CORRECT LEARNING MODEL
1 1.1 A RECTANGLE LEARNING GAME 1 1.2 A GENERAL MODEL 6 1.2.1 DEFINITION
OF THE PAC MODEL 7 1.2.2 REPRESENTATION SIZE AND INSTANCE DIMENSION 12
1.3 LEARNING BOOLEAN CONJUNCTIONS 16 1.4 INTRACTABILITY OF LEARNING
3-TERM DNF FORMULAE 18 1.5 USING 3-CNF FORMULAE TO AVOID INTRACTABILITY
22 1.6 EXERCISES 26 1.7 BIBLIOGRAPHIC NOTES 28 2 OCCAM'S RAZOR 31 2.1
OCCAM LEARNING AND SUCCINCTNESS 33 VI " CONTENTS 2.2 IMPROVING THE
SAMPLE SIZE FOR LEARNING CONJUNCTIONS 37 2.3 LEARNING CONJUNCTIONS WITH
FEW RELEVANT VARIABLES 38 2.4 LEARNING DECISION LISTS 42 2.5 EXERCISES
44 2.6 BIBLIOGRAPHIC NOTES 46 3 THE VAPNIK-CHERVONENKIS DIMENSION 49 3.1
WHEN CAN INFINITE CLASSES BE LEARNED WITH A FINITE SAMPLE? 49 3.2 THE
VAPNIK-CHERVONENKIS DIMENSION 50 3.3 EXAMPLES OF THE VC DIMENSION 51 3.4
A POLYNOMIAL BOUND ON |N C (5)| 54 3.5 A POLYNOMIAL BOUND ON THE SAMPLE
SIZE FOR PAC LEARNING 57 3.5.1 THE IMPORTANCE OF E-NETS 57 3.5.2 A SMALL
E-NET FROM RANDOM SAMPLING 59 3.6 SAMPLE SIZE LOWER BOUNDS 62 3.7 AN
APPLICATION TO NEURAL NETWORKS 64 3.8 EXERCISES 67 3.9 BIBLIOGRAPHIC
NOTES 70 4 WEAK AND STRONG LEARNING 73 4.1 A RELAXED DEFINITION OF
LEARNING? 73 4.2 BOOSTING THE CONFIDENCE 76 CONTENTS VII 4.3 BOOSTING
THE ACCURACY 78 4.3.1 A MODEST ACCURACY BOOSTING PROCEDURE 79 4.3.2
ERROR ANALYSIS FOR THE MODEST PROCEDURE 81 4.3.3 A RECURSIVE ACCURACY
BOOSTING ALGORITHM 85 4.3.4 BOUNDING THE DEPTH OF THE RECURSION 88 4.3.5
ANALYSIS OF FILTERING EFFICIENCY 89 4.3.6 FINISHING UP 96 4.4 EXERCISES
101 4.5 BIBLIOGRAPHIC NOTES - 102 5 LEARNING IN THE PRESENCE OF NOISE
103 5.1 THE CLASSIFICATION NOISE MODEL 104 5.2 AN ALGORITHM FOR LEARNING
CONJUNCTIONS FROM STATISTICS 106 5.3 THE STATISTICAL QUERY LEARNING
MODEL 108 5.4 SIMULATING STATISTICAL QUERIES IN THE PRESENCE OF NOISE
111 5.4.1 A NICE DECOMPOSITION OF P X 112 5.4.2 SOLVING FOR AN ESTIMATE
OF P X 114 5.4.3 GUESSING AND VERIFYING THE NOISE RATE 115 5.4.4
DESCRIPTION OF THE SIMULATION ALGORITHM 117 5.5 EXERCISES 119 5.6
BIBLIOGRAPHIC NOTES 121 VIII ({ CONTENTS 6 INHERENT UNPREDICTABILITY 123
6.1 REPRESENTATION DEPENDENT AND INDEPENDENT HARDNESS 123 6.2 THE
DISCRETE CUBE ROOT PROBLEM 124 6.2.1 THE DIFFICULTY OF DISCRETE CUBE
ROOTS 126 6.2.2 DISCRETE CUBE ROOTS AS A LEARNING PROBLEM 128 6.3 SMALL
BOOLEAN CIRCUITS ARE INHERENTLY UNPREDICTABLE 131 6.4 REDUCING THE DEPTH
OF INHERENTLY UNPREDICTABLE CIRCUITS 133 6.4.1 EXPANDING THE INPUT 135
6.5 A GENERAL METHOD AND ITS APPLICATION TO NEURAL NETWORKS 139 6.6
EXERCISES 140 6.7 BIBLIOGRAPHIC NOTES 141 7 REDUCIBILITY IN PAC LEARNING
143 7.1 REDUCING DNF TO MONOTONE DNF 144 7.2 A GENERAL METHOD FOR
REDUCIBILITY 147 7.3 REDUCING BOOLEAN FORMULAE TO FINITE AUTOMATA 149
7.4 EXERCISES 153 7.5 BIBLIOGRAPHIC NOTES 154 8 LEARNING FINITE AUTOMATA
BY EXPERIMENTATION 155 8.1 ACTIVE AND PASSIVE LEARNING 155 8.2 EXACT
LEARNING USING QUERIES 158 CONTENTS IX 8.3 EXACT LEARNING OF FINITE
AUTOMATA 160 8.3.1 ACCESS STRINGS AND DISTINGUISHING STRINGS 160 8.3.2
AN EFFICIENTLY COMPUTABLE STATE PARTITION 162 8.3.3 THE TENTATIVE
HYPOTHESIS M 164 8.3.4 USING A COUNTEREXAMPLE 166 8.3.5 THE ALGORITHM
FOR LEARNING FINITE AUTOMATA 169 8.3.6 RUNNING TIME ANALYSIS 171 8.4
LEARNING WITHOUT A RESET 174 8.4.1 USING A HOMING SEQUENCE TO LEARN 176
8.4.2 BUILDING A HOMING SEQUENCE USING OVERSIZED GEN- ERALIZED
CLASSIFICATION TREES 178 8.4.3 THE NO-RESET ALGORITHM 181 8.4.4 MAKING
SURE L A BUILDS GENERALIZED CLASSIFICATION TREES 182 8.5 EXERCISES 185
8.6 BIBLIOGRAPHIC NOTES 186 9 APPENDIX: SOME TOOLS FOR PROBABILISTIC
ANALYSIS 189 9.1 THE UNION BOUND 189 9.2 MARKOV'S INEQUALITY 189 9.3
CHERNOFF BOUNDS 190 CONTENTS 193 BIBLIOGRAPHY 205 INDEX |
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| author | Kearns, Michael J. Vazirani, Vijay V. 1957- |
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| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
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| institution | BVB |
| isbn | 0262111934 9780262111935 |
| language | English |
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| physical | XII, 207 S. graph. Darst. |
| publishDate | 1994 |
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| publisher | MIT Press |
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| spelling | Kearns, Michael J. Verfasser aut An introduction to computational learning theory Michael J. Kearns ; Umesh V. Vazirani Cambridge, Mass. [u.a.] MIT Press 1994 XII, 207 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Computer (DE-588)4070083-5 gnd rswk-swf Lerntheorie (DE-588)4114402-8 gnd rswk-swf Computer (DE-588)4070083-5 s Lerntheorie (DE-588)4114402-8 s DE-604 Vazirani, Vijay V. 1957- Verfasser (DE-588)122932196 aut HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006772526&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kearns, Michael J. Vazirani, Vijay V. 1957- An introduction to computational learning theory Computer (DE-588)4070083-5 gnd Lerntheorie (DE-588)4114402-8 gnd |
| subject_GND | (DE-588)4070083-5 (DE-588)4114402-8 |
| title | An introduction to computational learning theory |
| title_auth | An introduction to computational learning theory |
| title_exact_search | An introduction to computational learning theory |
| title_full | An introduction to computational learning theory Michael J. Kearns ; Umesh V. Vazirani |
| title_fullStr | An introduction to computational learning theory Michael J. Kearns ; Umesh V. Vazirani |
| title_full_unstemmed | An introduction to computational learning theory Michael J. Kearns ; Umesh V. Vazirani |
| title_short | An introduction to computational learning theory |
| title_sort | an introduction to computational learning theory |
| topic | Computer (DE-588)4070083-5 gnd Lerntheorie (DE-588)4114402-8 gnd |
| topic_facet | Computer Lerntheorie |
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