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Semisupervised learning from different information sources. Knowledge and Information Systems 7(3):289–309

By Tao Li and Mitsunori Ogihara

Abstract

Abstract. This paper studies the use of a semisupervised learning algorithm from different information sources. We first offer a theoretical explanation as to why minimising the disagreement between individual models could lead to the performance improvement. Based on the observation, this paper proposes a semisupervised learning approach that attempts to minimise this disagreement by employing a co-updating method and making use of both labeled and unlabeled data. Three experiments to test the effectiveness of the approach are presented in this paper: (i) webpage classification from both content and hyperlinks; (ii) functional classification of gene using gene expression data and phylogenetic data and (iii) machine self-maintaining from both sensory and image data. The results show the effectiveness and efficiency of our approach and suggest its application potentials

Topics: Decision tree, Minimise disagreement, Semisupervised, Support vector machines, Unlabelled data
Year: 2005
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.5251
Provided by: CiteSeerX
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