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Regularized empirical risk minimization including support vector machines plays an important role in machine learning theory. In this paper regularized pairwise learning (RPL) methods based on kernels will be investigated. One example is regularized minimization of the error entropy loss which has recently attracted quite some interest from the viewpoint of consistency and learning rates. This paper...
A novel method for achieving practical real-time traffic classification is proposed in this paper, which is based on C4.5 decision tree. Most existing traffic classification algorithms only focus on accuracy of the classification results, but lack of considering the various costs in actual deployment. So they cannot guarantee that the obtained tree construction is optimal for hardware and software...
PSO has been proved as an effective supervised learning system in recent years, but it's not an effective method for incremental learning problems. Aiming at the incremental learning target for classification, a hybrid algorithm of Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) called Immune based PSO (IPSO) is presented in this paper. IPSO inherits the incremental learning ability...
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