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The evaluation of competitive power is very important for bidder in power system, how to improve the accuracy and efficiency of evaluation is the keystone people pay attention to, and many researches have been done around it. A combined model of least squares support vector machines optimized by an improved particle swarm optimization algorithm is proposed in this paper to do evaluate the competitive...
The pre-diagnosis to type 2 diabetes, and the effective prophylaxis and treatment of its complication is to be worthy paying attention to. So an intelligent diagnosis based on quantum particle swarm optimization (QPSO) algorithm and weighted least squares support vector machines (WLS-SVM) is presented, which can overcome the disadvantage of large sample data, slow model-building and rather large deviation...
The selection for hyper-parameters including kernel parameters and the regularization is important to the performance of least squares support vector machines (LS-SVM). The existed parameters selection algorithms, such as the analytical, algebraic techniques and particle swarm optimization (PSO) algorithm, have their own shortcomings. In this paper, the problem of model selection for LS-SVM is discussed...
Common used parameters selection method for support vector machines (SVM) is cross-validation, which is complicated calculation and takes a very long time. In this paper, a novel regularization parameter and kernel parameter tuning approach of SVM is presented based on quantum particle swarm optimization algorithm (QPSO). QPSO is a particle swarm optimization (PSO) with quantum individual that has...
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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