Availability: Inductive inference for large scale text classification

Inductive inference for large scale text classification: Kernel approaches and techniques

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Bibliographic Details
Main Authors:	Silva, Catarina (Author), Ribeiro, Bernardete (Author)
Format:	Book
Language:	English
Published:	Berlin [u.a.] Springer 2010
Series:	Studies in computational intelligence 255
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020446326&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Physical Description:	XX, 155 S. graph. Darst.
ISBN:	9783642045325

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Record in the Search Index

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adam_text	CONTENTS PART I: FUNDAMENTALS 1 BACKGROUND ON TEXT CLASSIFICATION 3 1.1 PROBLEM SETTING 3 1.2 APPLICATIONS OF TEXT CLASSIFICATION 5 1.2.1 DOCUMENT ORGANIZATION 5 1.2.2 TEXT FILTERING 5 1.2.3 WORD SENSE DISAMBIGUATION 5 1.2.4 OTHER APPLICATIONS 6 1.3 DOCUMENT REPRESENTATION 6 1.4 PRE-PROCESSING TEXT 8 1.4.1 FEATURE SELECTION 8 1.4.2 FEATURE EXTRACTION 10 1.5 CLASSIFIERS 11 1.5.1 ROCCHIO S METHOD 12 1.5.2 DECISION TREES AND RULES 13 1.5.3 NAIVE BAYES 15 1.5.4 K-NEAREST NEIGHBOR 15 1.5.5 NEURAL NETWORKS 16 1.5.6 KERNEL-BASED LEARNING MACHINES 17 1.5.7 COMMITTEES 18 1.5.8 ACTIVE LEARNING 20 1.5.9 OTHER METHODS 21 1.6 EVALUATION 21 1.6.1 PERFORMANCE CRITERIA 22 1.6.2 DOCUMENT CORPORA 24 1.7 EVALUATION OF PRE-PROCESSING METHODS 26 1.8 CONCLUSION 29 BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/995947503 DIGITALISIERT DURCH 4.5 CONCLUSION 89 XIV CONTENTS 2 KERNEL MACHINES FOR TEXT CLASSIFICATION 31 2.1 KERNEL METHODS 31 2.2 SUPPORT VECTOR MACHINES 32 2.2.1 LINEAR HARD-MARGIN SVMS 33 2.2.2 SOFT-MARGIN SVMS 36 2.2.3 NONLINEAR SVMS 37 2.3 RELEVANCE VECTOR MACHINES 38 2.3.1 BAYESIAN APPROACHES 39 2.3.2 RVM APPROACH 40 2.4 BASELINE KERNEL MACHINES PERFORMANCES WITH BENCHMARK CORPORA 43 2.4.1 SVM PERFORMANCE 44 2.4.2 RVM PERFORMANCE 46 2.4.3 DISCUSSION 47 2.5 CONCLUSION 48 PART II: APPROACHES AND TECHNIQUES 3 ENHANCING SVMS FOR TEXT CLASSIFICATION 51 3.1 INCORPORATING UNLABELED DATA 51 3.1.1 BACKGROUND KNOWLEDGE AND ACTIVE LEARNING 53 3.1.2 EXPERIMENTAL RESULTS 56 3.1.3 COMBINING BACKGROUND KNOWLEDGE AND ACTIVE LEARNING 59 3.1.4 ANALYSIS OF RESULTS 60 3.2 USING MULTIPLE CLASSIFIERS 63 3.2.1 SVM ENSEMBLES 65 3.2.2 EXPERIMENTAL RESULTS AND ANALYSIS 66 3.3 CONCLUSION 69 4 SCALING RVMS FOR TEXT CLASSIFICATION 71 4.1 INTRODUCTION 71 4.2 SCALE REDUCTION APPROACHES 72 4.2.1 ACTIVE LEARNING 73 4.2.2 SIMILITUDE MEASURE 76 4.3 DIVIDE-AND-CONQUER APPROACHES 78 4.3.1 INCREMENTAL RVM 79 4.3.2 RVM BOOSTING 80 4.3.3 RVM ENSEMBLE 83 4.3.4 ANALYSIS OF RESULTS 84 4.4 HYBRID RVM-SVM APPROACH 86 CONTENTS XV 5 DISTRIBUTING TEXT CLASSIFICATION IN GRID ENVIRONMENTS ... 93 5.1 INTRODUCTION 93 5.2 RELATED WORK 94 5.2.1 DISTRIBUTED COMPUTING PLATFORMS 94 5.2.2 DISTRIBUTED APPLICATIONS 95 5.3 DEPLOYMENT IN THE DISTRIBUTED ENVIRONMENT 97 5.3.1 TASK SCHEDULING AND DIRECT ACYCLIC GRAPHS 97 5.3.2 DAG DESIGN IN A DISTRIBUTED ENVIRONMENT 97 5.3.3 DISTRIBUTED ENVIRONMENT FOR THE EXPERIMENTAL SETUP 100 5.3.4 MODEL OF THE ENVIRONMENT 100 5.4 DESIGN OF DISTRIBUTED TEXT CLASSIFICATION SCHEDULING SCHEMES 102 5.4.1 DATAFLOW IN TEXT CLASSIFICATION 102 5.4.2 OPTIMIZATION OF SCHEDULING SCHEMES 104 5.5 EXPERIMENTAL RESULTS 108 5.5.1 PROCESSING TIME 109 5.5.2 CLASSIFICATION PERFORMANCE 112 5.5.3 DISCUSSION OF RESULTS 114 5.6 CONCLUSION 115 6 FRAMEWORK FOR TEXT CLASSIFICATION 117 6.1 NOVEL TRENDS IN TEXT CLASSIFICATION 122 6.1.1 INFORMATION SEMANTICS 123 6.1.2 INFORMATION EXTRACTION 124 6.1.3 INFORMATION DISTRIBUTED SYSTEMS 126 6.2 CONCLUSION 127 A REUTERS-21578 129 A.I INTRODUCTION 129 A.2 HISTORY 129 A.3 FORMATTING 130 A.4 THE REUTERS TAG 130 A.5 DOCUMENT-INTERNAL TAGS 132 A.6 CATEGORIES 133 A.7 USING REUTERS-21578 FOR TEXT CATEGORIZATION RESEARCH 134 A.7.1 THE MODIFIED LEWIS ( MODLEWIS ) SPLIT 135 A. XVI CONTENTS B.3.1 TOPIC CODES 140 B.3.2 CODING POLICY 142 B.4 STOPWORDS 142 REFERENCES 143 INDEX 153
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spellingShingle	Silva, Catarina Ribeiro, Bernardete Inductive inference for large scale text classification Kernel approaches and techniques Studies in computational intelligence
title	Inductive inference for large scale text classification Kernel approaches and techniques
title_auth	Inductive inference for large scale text classification Kernel approaches and techniques
title_exact_search	Inductive inference for large scale text classification Kernel approaches and techniques
title_full	Inductive inference for large scale text classification Kernel approaches and techniques Catarina Silva and Bernadete Ribeiro
title_fullStr	Inductive inference for large scale text classification Kernel approaches and techniques Catarina Silva and Bernadete Ribeiro
title_full_unstemmed	Inductive inference for large scale text classification Kernel approaches and techniques Catarina Silva and Bernadete Ribeiro
title_short	Inductive inference for large scale text classification
title_sort	inductive inference for large scale text classification kernel approaches and techniques
title_sub	Kernel approaches and techniques
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020446326&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV020822171
work_keys_str_mv	AT silvacatarina inductiveinferenceforlargescaletextclassificationkernelapproachesandtechniques AT ribeirobernardete inductiveinferenceforlargescaletextclassificationkernelapproachesandtechniques

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