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Multi-task learning for one-class SVM with additional new features

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Abstract

In real applications of one class classification, new features may be added due to some practical or technical reason. While lacking of representative samples for the new features, multi-task learning idea could be used to bring some information from the former learning model. Based on the above assumption, a new multi-task learning approach is proposed to deal with the training of the updated system when adding new measurements. In the model, a parameter is introduced to control the information needed from the former model and an heuristic search method is also established to get a corresponding proper value. Experiments conducted on toy data and real data set show that the new method could decrease the probability of false positive rapidly, while keeping the probability of false negative approximately stable as the number of samples for new introduced features increases.
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hal-02353210 , version 1 (07-11-2019)

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Yongjian Xue, Pierre Beauseroy. Multi-task learning for one-class SVM with additional new features. 2016 23rd International Conference on Pattern Recognition (ICPR), Dec 2016, Cancun, Mexico. pp.1571-1576, ⟨10.1109/ICPR.2016.7899861⟩. ⟨hal-02353210⟩
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