Abstract
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.
