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• W1529958053 abstract "As the conventional training algorithms of least squares support vector machines (LS-SVM) are inefficient in online applications, an online learning algorithm is proposed. The online algorithm is suitable for the large data set and practical applications where the data come in sequentially. Aiming at the characteristics of signals, a wavelet kernel satisfying wavelet frames is presented. The wavelet kernel can approximate arbitrary functions in quadratic continuous integral space, hence the generalization ability of LS-SVM is improved. To illustrate its favorable performance, the wavelet based online LS-SVM (WOLS-SVM) is applied to nonlinear system identification. The simulation results show that the WOLS-SVM outperforms the existing algorithms with higher learning efficiency as well as better accuracy, and indicate its effectiveness." @default.
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• W1529958053 date "2007-07-16" @default.
• W1529958053 modified "2023-09-23" @default.
• W1529958053 title "Online Least Squares Support Vector Machines Based on Wavelet and Its Applications" @default.
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• W1529958053 doi "https://doi.org/10.1007/978-3-540-72395-0_53" @default.
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