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Support Vector Machine (SVM) classification requires set of one or more parameters and these parameters have significant influence on classification precision and generalization ability. Searching for suitable model parameters invokes great computational load, which accentuates with increasing size of the dataset and with amount of the parameters being optimized. In this paper we present and compare...
PSO is an overall stochastic optimization algorithm based on the selection of the feature set and optimization of kernel function parameters, which has great impact on the forecasting performance of support vector machines (SVM) model. This paper presents the combining model (PSO-SVM) of the particle swarm optimization and support vector machine. This model uses the PSO to conduct the optimization...
Multi-scale kernel function learning is a special case of multi-kernel learning, namely combines several multi-scale kernels. This approach is more flexible. It provides more comprehensive choice of scale than the mixed kernel learning. In this paper, the model's parameters of multi-scale Gaussian kernel were used as elementary particles. The parameters of multi-scale Gaussian kernel were global optimized...
Statistical Learning Theory focuses on the machine learning theory for small samples. Support vector machine (SVM) are new methods based on statistical learning theory. There are many kinds of function can be used for kernel of SVM. Wavelet function is a set of bases that can approximate arbitrary functions in arbitrary precision. So Marr wavelet was used to construct wavelet kernel. On the other...
To choose an appropriate kernel function is one major task for SVM. Different kernel functions will produce different SVMs and may result in different performances. Combined kernel function shows more stable and higher performance than single kernel function, so there is a need to optimize the combined kernel function to enhance the generalization capability of SVM. This paper proposes to optimize...
Based on quantum particle swarm optimization algorithm (QPSO), a novel approach of constructing multi-class least squares wavelet SVM (LS-WSVM) classifiers is presented, regularization parameters and kernel parameters of LS-WSVM can be optimized. Quantum particle swarm optimization can get appropriate parameters of LS-WSVM with global search, so the LS-WSVM model for the multi-class classifiers is...
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