Learning with privileged information using Bayesian networks |
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Authors: | Shangfei WANG Menghua HE Yachen ZHU Shan HE Yue LIU Qiang JI |
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Affiliation: | 1. School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China2. Key Lab of Computing and Communicating Software of Anhui Province, Hefei 230027, China3. School of Mathematical Sciences, University of Science and Technology of China, Hefei 230027, China4. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy NY 12180-3590, USA |
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Abstract: | For many supervised learning applications, additional information, besides the labels, is often available during training, but not available during testing. Such additional information, referred to the privileged information, can be exploited during training to construct a better classifier. In this paper, we propose a Bayesian network (BN) approach for learning with privileged information. We propose to incorporate the privileged information through a three-node BN. We further mathematically evaluate different topologies of the three-node BN and identify those structures, through which the privileged information can benefit the classification. Experimental results on handwritten digit recognition, spontaneous versus posed expression recognition, and gender recognition demonstrate the effectiveness of our approach. |
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Keywords: | Bayesian network privileged information classification maximum likelihood estimation |
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