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Classification Algorithm Based on Single Sample


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Classification Algorithm Based on Single Sample
PAN Zhi-song1YAN Ji-kun2YANG Xu-bing3MIAO Zhi-min1CHEN Bin3
1.Institute of Command Automation,PLA University of Science and Technology,Nanjing 210007,China;2.The West-South Electronics Institute,Chengdu 610041,China;3.Department of ComputerScience and Engineering,Nanjing University of Aeronautics & Astronautics,Na
single samples kernel means classification support vectors
In order to solve the extreme situation that only a few target examples or only one can be used in training the classification,a single sample classification algorithm is presented here.Spherical surfaces are applied as classified hypersphere,and the largest radius can be obtained enclosing the single sample under the restriction that all outliers are outside the hypersphere.It fails when the distribution of input patterns is complex.The classifier applies kernel means,performing a nonlinear data transformation into some high dimensional feature space,increases the probability of the linear separability of the patterns within the feature space and therefore solves the original classification problem.The paper verifies that the algorithm can effectively deal with the unbalanced data classification on various synthetic and UCI datasets.


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Last Update: 2012-11-19