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Support vector regression and regularization networks are kernel-based techniques for solving the regression problem of recovering the unknown function from sample data. The choice of the kernel function, which determines the mapping between the input space and the feature space, is of crucial importance to such learning machines. Estimating the irregular function with a multiscale structure that...
Based on quantum-inspired evolutionary algorithm (QEA), a novel approach of constructing multi-class least squares wavelet SVM (LS-WSVM) ensemble classifiers is presented, regularization parameters and kernel parameters of LS-WSVM can be optimized. Quantum-inspired evolutionary optimization can get appropriate parameters of LS-WSVM with global search, so the LS-WSVM ensemble model with boosting for...
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