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- W2363235493 abstract "Aiming at the computational complexity of LS-SVM's on-line modeling,a dynamic non-bias least square support vector regression model is proposed.The model eliminates the bias of LS-SVM by improving the form of structure risk.As a result,the non-bias LS-SVM is achieved and the calculation method of regression coefficients is simplified.Then an online learning algorithm based on the Cholesky factorization is designed according to the character of kernel function matrix in the model's dynamic change process.The improved learning algorithm can make full use of the historical training results and reduce the computational complexity.Experimental results indicate the effectiveness of the dynamic non-bias LS-SVM." @default.
- W2363235493 created "2016-06-24" @default.
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- W2363235493 date "2008-01-01" @default.
- W2363235493 modified "2023-09-22" @default.
- W2363235493 title "Dynamic non-bias LS-SVM learning algorithm based on Cholesky factorization" @default.
- W2363235493 hasPublicationYear "2008" @default.
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