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Semi-supervised separability-maximum boosting


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Semi-supervised separability-maximum boosting
Hou JieMao YaobinSun Jinsheng
School of Automation,NUST,Nanjing 210094,China
semi-supervised learning boosting algorithm separability classifier
The semi-supervised learning often fails to improve classifier accuracy when the data distribution does not match its semi-supervised assumption.In this paper,the semi-supervised separability-maximum boosting(Semi-SMBoost),a novel semi-supervised boosting algorithm based on local minimizing in high-density region and global maximizing in sample space criterion is proposed.The criterion utilizes both the cluster assumption and the manifold assumption of the semi-supervised learning,and reduces the accuracy regression caused by miss matching of data distribution and semi-supervised assumption.Experiment results indicate that the Semi-SMBoost improves classification accuracy on both clustered data and manifold data on,and stabilizes the classifier on noised data.


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Last Update: 2014-10-31