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Multitask Learning has been proven to be more effective than the traditional single task learning on many real-world problems by simultaneously transferring knowledge among different tasks which may suffer from limited labeled data. However, in order to build a reliable multitask learning model, nontrivial effort to construct the relatedness between different tasks is critical. When the number of...
Genetic Programming (GP) has been successfully applied to supervised classification problems. This work evaluates a tree-based GP implementation in a one-class classification scenario, using artificial outliers generated by a promising method recently developed by Bánhalmi et al. The proposed approach does not require the use of certain techniques employed by related works, thus providing a simpler...
In recent years, machine learning technology often used as a recognition method of anomaly in anomaly detection. In this paper we have proposed a One-class small hypersphere support vector machine classifier (OCSHSVM) algorithm, which builds a learning classifier model via both normal and abnormal network traffic. This combination of normal and abnormal traffic for training model gives the better...
Support vector machines (SVM) can overcome the disadvantage of traditional anomaly detection, which need large sample data and have great effect in real-time detection, but has the disadvantage of slow training velocity. Least squares support vector machines (LS-SVM) can overcome the disadvantage of slow training velocity, but makes the solution lose sparsity and robustness. So a weighted LS-SVM (WLS-SVM)...
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