Progressive similarity transductive support vector machine algorithm for small sample text classification

被引：0

作者：

Ma, Jianbin ^{[1
]}

Li, Ying ^{[2
]}

机构：

[1] College of Information Science and Technology, Agricultural University of Hebei, Hebei, 071001, Baoding

[2] College of Economic and Trade, Agricultural University of Hebei, Hebei, 071001, Baoding

来源：

Information Technology Journal | 2013年 / 12卷 / 23期

关键词：

PSTS VM; Small sample; Support Vector Machine; Text classification;

D O I：

10.3923/itj.2013.7673.7676

中图分类号：

学科分类号：

摘要：

Support Vector Machine (SVM) algorithm is applied to text classification widely. However, SVM's limitation is that it is difficult to label samples rightly if available training samples are small. So TSVM (Transductive Support Vector Machine) was introduced to minimize misclassification of test samples via., training on labeled and unlabeled samples. However, in the training process of TSVM, the parameter N (the number of positive samples) should be inputted artificially. The parameter N is difficult to estimate. In this study, PSTSVM (Progressive Similarity Transductive Support Vector Machine) was introduced which labeled most likely unlabeled samples pairwise by similarity computing and then retrained to readjust the hyperplane. The experimental results on Reuters dataset showed that PSTSVM algorithm was effective on a mixed training set of unlabeled samples and labeled samples. © 2013 Asian Network for Scientific Information.

引用

页码：7673 / 7676

页数：3

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