Circuits, Communications and Systems, Pacific-Asia Conference on (2009)
May 16, 2009 to May 17, 2009
The location of logistic center directly influences the operational effect of the enterprise. Support vector machine (SVM) has been applied to regression widely. However, if the index of the training data has much noise and redundancy, the generalized performance of SVM will be weakened, so this can cause some disadvantages of slow convergence speed and low regression accuracy. A SVM regression model based on principal component analysis (PCA-SVM) is presented in this paper, using principal component analysis to reduce the dimensionality of indexes, and then extract principal components to replace the original indexes, and both processing speed and regression accuracy will be improved. At last, apply this model to logistic centre location, and it shows more generalized performance and better regression accuracy compared with the method of single SVM and BP neural networks.
logistic center location, PCA, SVM
M. Zhang, Z. Ji and Z. Zhang, "Evaluate the Selection of Logistics Centre Location Using SVM Based on Principal Component Analysis," 2009 Pacific-Asia Conference on Circuits, Communications and Systems (PACCS 2009)(PACCS), Chengdu, 2009, pp. 661-664.