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In this paper, an efficient support vector machine (SVM) algorithm for solving multi-class pattern recognition problems is proposed. The samples in each class are trained by one-class SVM (OCSVM), respectively. And then several sets of support vectors (SVs) are obtained, which well express the distribution of the original training samples. These SVs finally are combined into a set of training samples...
In this paper, we propose a learning method for fast training of support vector machines (SVMs). First, we divide the two-class training samples into two sets according to the labels. Secondly, the two set one-class samples are trained by using one-class SVM (OCSVM) respectively, and we get two set support vectors (SVs). Finally, the two set SVs are combined into a set of two-class training samples...
Selecting a small number of relevant genes for accurate classification of samples is essential for the development of diagnostic tests, which have been the subject of considerable research in the past few years. However, many researches have still been trying to improve the algorithms to obtain better results. Here we present a novel implementation of recursive feature elimination method (nRFE) for...
We proposed a new approach for gene selection and multi-cancer classification based on step-by-step improvement of classification performance (SSiCP). The SSiCP gene selection algorithms were evaluated over the NCI60 and GCM benchmark datasets, with an accuracy of 96.6% and 95.5% in 10-fold cross validation, respectively. Furthermore, the SSiCP outperformed recently published algorithms when applied...
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