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《Acta Electronica Sinica》 2008-S1
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A Random Subspace Method for Co-Training

WANG Jiao,LUO Si-wei,ZENG Xian-hua(School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China)  
Semi-supervised learning has received much attention recently.Cotraining is a kind of semi-supervised learning method which uses unlabeled data to boost the performance of standard supervised learning algorithms.A novel co-training style algorithm,RASCO(for RAndom Subspace CO-training),is proposed which uses stochastic discrimination theory to extend co-training to multi-view situation.The accuracy and generalizability of RASCO are analyzed.The influences of the parameters of RASCO are discussed.Experiments on UCI data set demonstrate that RASCO is more effective than other co-training style algorithms.
【Fund】: 教育部博士点基金(No.20050004001 No.200800041049);; 国家863高技术研究发展计划(No.2007AA01Z168);; 国家自然科学基金(No.60773016 No.60805041 No.60872082)
【CateGory Index】: TP181
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