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- W2147505890 abstract "In this paper, we address the problem of one-class classification. Taking into account the fact that in some applications, the given training samples are rather limited, we attempt to utilize the advantages of Multi-task Learning (MTL), where the data of related tasks may share similar structure and helpful information. We then propose an MTL framework for one-class classification. The framework derives from the one-class v-SVM and makes use of related tasks by constraining them to have similar solutions. This formulation can be cast into a second-order cone program, which achieves a global solution and is solved efficiently. Further, the framework also maintains the favorable property of the v parameter in the v-SVM, which can control the fraction of outliers and support vectors, in one-class classification. This framework also connects with several existing models. Experimental results on both synthetic and real-world datasets demonstrate the properties and advantages of our proposed model." @default.
- W2147505890 created "2016-06-24" @default.
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- W2147505890 date "2010-07-01" @default.
- W2147505890 modified "2023-10-15" @default.
- W2147505890 title "Multi-task Learning for one-class classification" @default.
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- W2147505890 doi "https://doi.org/10.1109/ijcnn.2010.5596881" @default.
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