標題: Semi-Supervised Text Classification With Universum Learning
作者: Liu, Chien-Liang
Hsaio, Wen-Hoar
Lee, Chia-Hoang
Chang, Tao-Hsing
Kuo, Tsung-Hsun
資訊工程學系
Department of Computer Science
關鍵字: AdaBoost;learning with Universum;text classification
公開日期: 二月-2016
摘要: Universum, a collection of nonexamples that do not belong to any class of interest, has become a new research topic in machine learning. This paper devises a semi-supervised learning with Universum algorithm based on boosting technique, and focuses on situations where only a few labeled examples are available. We also show that the training error of AdaBoost with Universum is bounded by the product of normalization factor, and the training error drops exponentially fast when each weak classifier is slightly better than random guessing. Finally, the experiments use four data sets with several combinations. Experimental results indicate that the proposed algorithm can benefit from Universum examples and outperform several alternative methods, particularly when insufficient labeled examples are available. When the number of labeled examples is insufficient to estimate the parameters of classification functions, the Universum can be used to approximate the prior distribution of the classification functions. The experimental results can be explained using the concept of Universum introduced by Vapnik, that is, Universum examples implicitly specify a prior distribution on the set of classification functions.
URI: http://dx.doi.org/10.1109/TCYB.2015.2403573
http://hdl.handle.net/11536/132863
ISSN: 2168-2267
DOI: 10.1109/TCYB.2015.2403573
期刊: IEEE TRANSACTIONS ON CYBERNETICS
Volume: 46
Issue: 2
起始頁: 462
結束頁: 473
顯示於類別:期刊論文