Semisupervised learning from different information sources |
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Authors: | Tao Li Mitsunori Ogihara |
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Affiliation: | (1) Department of Computer Science, University of Rochester, Rochester, NY 14627-0226, USA |
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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. |
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Keywords: | Decision tree Minimise disagreement Semisupervised Support vector machines Unlabelled data |
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