A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Information Retrieval
Training Invariant Support Vector Machines
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Active Learning for Natural Language Parsing and Information Extraction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
An empirical study of active learning with support vector machines for Japanese word segmentation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Virtual relevant documents in text categorization with support vector machines
Information Processing and Management: an International Journal
Incorporating topical support documents into a small training set in text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
POSBIOTM-NER in the shared task of BioNLP/NLPBA 2004
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
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We explore how virtual examples (artificially created examples) improve performance of text classification with Support Vector Machines (SVMs). We propose techniques to create virtual examples for text classification based on the assumption that the category of a document is unchanged even if a small number of words are added or deleted. We evaluate the proposed methods by Reuters-21758 test set collection. Experimental results show virtual examples improve the performance of text classification with SVMs, especially for small training sets.