A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in 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
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Virtual examples for text classification with Support Vector Machines
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Query enrichment for web-query classification
ACM Transactions on Information Systems (TOIS)
Virtual relevant documents in text categorization with support vector machines
Information Processing and Management: an International Journal
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This paper explores the incorporation of topical support documents into a training set as a means of compensating for a shortage of positive training data in text categorization. To support topical representation, our method applies a simple transformation to documents, i.e., making new documents from existing positive documents by squaring a conventional term weight. The topical support documents thus created not only are expected to preserve the topic, but even improve the topical representation by emphasizing terms with higher weights. Experiments with support vector machines showed the effectiveness on RCV1 collection with a small number of positive training data. Our topical support representation achieved 52.01% and 8.83% improvements for 33 and 56 categories of RCV1 Topic in micro-averaged F1 with less than 100 and 300 positive documents in learning, respectively. Result analyses based on robustness indicate that topical support documents contribute to a steady and stable improvement.