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We consider a semi-supervised regression setting where we have temporal sequences of partially labeled data, under the assumption that the labels should vary slowly along a sequence, but that nearby points in input space may have drastically different labels. The setting is motivated by problems such as determining the time of the day or the level of air visibility given an image of a landscape, which...
In order to simulate human behavior to achieve intelligent control, in this paper a mathematical modeling method is presented based on Kernel Principal Component Analysis (KPCA) and Support Sector Machine. Sample data from the input space are mapped to high-dimensional feature space by non-linear transformation, then their features are extracted by PCA to decrease dimension of input vector and then...
Distribution centers site selection has become a popular problem in recent years. Fine distribution centers site selection can ensure the supply and reduce the cost. By studying the methods proposed by other scholars, a mew method, KPCA (kernel principal component analysis) -SVRM (support vector regression machine) is proposed by this paper. The first step of this method is to apply KPCA to SVRM for...
A new feature extraction method for high dimensional data using least squares support vector regression (LSSVR) is presented. Firstly, the expressions of optimal projection vectors are derived into the same form as that in the LSSVR algorithm by specially extending the feature of training samples. So the optimal projection vectors could be obtained by LSSVR. Then, using the kernel tricks, the data...
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