Abstract
In this paper, we derive a new one-class Support Vector Machine (SVM) based on hidden information. Taking into account the fact that in some applications, the training instances are rather limited, we attempt to utilize the additional information hidden in the training data. We demonstrate the performance of the new one-class SVM on several publicly available data sets from UCI machine learning repository and also present the comparison with the standard one-class SVM. The experimental results indicate the validity and advantage of the new one-class SVM.
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