Automatic text processing
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Using a generalized instance set for automatic text categorization
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The string B-tree: a new data structure for string search in external memory and its applications
Journal of the ACM (JACM)
Improving automatic Chinese text categorization by error correction
IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Machine learning for Arabic text categorization: Research Articles
Journal of the American Society for Information Science and Technology
Category mapping for the automatic integration of category-constrained web search
International Journal of Business Intelligence and Data Mining
Adapting centroid classifier for document categorization
Expert Systems with Applications: An International Journal
Hierarchical directory mapping for category-constrained meta-search
Journal of Intelligent Information Systems
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The goal of this paper is to derive extra representatives from each class to compensate for the potential weakness of linear classifiers that compute one representative for each class. To evaluate the effectiveness of our approach, we compared with linear classifier produced by Rocchio algorithm and the k-nearest neighbor (kNN) classifier. Experimental results show that our approach improved linear classifier and achieved micro-averaged accuracy close to that of kNN, with much less classification time. Furthermore, we could provide a suggestion to reorganize the structure of classes when identify new representatives for linear classifier.