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Support vector machine (SVM) algorithm has shown a good learning ability and generalization ability in classification, regression and forecasting. This paper mainly analyzes the the performance of support vector machine algorithm in the classification problem, including the algorithm in the kernel function selection, parameter optimization, and integration of other algorithms and to deal with multi-classification...
In order to improve the generalization performance of support vector machine (SVM), a kind of ensemble SVM using an entropy-based attribute selection method was proposed. An entropy metric based on similarity between objects was designed to evaluate the importance degree of each attribute and so as to obtain a set of important attributes. Based on the set of important attributes, the Bagging method...
Support vector machine(SVM) has become a powerful and widely used machine learning method in resent years. Gaussian kernel is the most commonly used kernel function. However, model selection including setting the width parameter sigma in kernel function and the regularization parameter C is essential to generalization performance of SVM. In this paper we proposed a new parameter selection method for...
Support Vector Machine (SVM) is a powerful classification technique based on the idea of structural risk minimization. Use of a kernel function enables the curse of dimensionality to be addressed. However, a proper kernel function for a certain problem is dependent on the specific dataset and as such there is no good method on how to choose a kernel function. In this paper, the choice of the kernel...
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