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Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method (BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters...
The feature subset selection is a key preprocessing part in the detection of the stored-grain insects based on the image recognition technology. According to the global optimization ability of the particle swarm optimization (PSO) and the superior classification performance of the support vector machines (SVM), this study proposed a method based on PSO and SVM to improve the classification accuracy...
In order to recognize stratums, a new support vector machine model (SVMM) is built on the basis of well-logging data and with RBF as its kernel function. Through the optimization of penalty parameter C and the introduction of a discriminant function, the classification accuracy of SVMM is greatly enhanced. Experiments show that the SVM classifier can be applied effectively to the recognition of stratums,...
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