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Decision-tree-based multiclass support vector machine (DTSVM) can solve the problem of unclassified regions that exists in the conventional SVM. But the classification precision and generalization ability of DTSVM classifier depends on the structure of the decision tree. In addition, the training speed of DTSVM becomes slower for more training samples. In this paper, a new measurement of inter-class...
In this research, we have extended the use of Kernel Dimensionality Reduction (KDR) in the context of semi supervised learning in particular for micro-array DNA clustering application. We have proposed a new model call K-means-KDR for survival analysis which we aimed to improve the genes classification performance and study the dimension of effective subspaces in cancer patient survival analysis....
This paper proposes an improved scheme of using the inter-cluster distance in the feature space to choose the kernel parameters. First, the candidate vectors of the training set are selected. Then calculate the inter-cluster distance between classes to choose the proper kernel parameters. Finally the selected kernel parameters are used to train the support vector machine (SVM) models. The basic principle...
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