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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...
In this paper, an evolutionary hybrid approach is studied for fault diagnosis and it is applied to classify the loopers faults in hot rolling process. The algorithm called evolutionary KPCA-LSSVM is the combination of genetic algorithm (GA), kernel principal component analysis (KPCA) and Least Squares Support Vector Machine (LSSVM), which can obtain better fault recognition rate. Firstly, kernel function...
In this paper, a new intrusion detection method based on support vector machines improved by artificial immunization algorithm is presented. Support vector machines (SVM) has been well recognized as a powerful computational tool for problems with nonlinearity had high dimensionalities. Right setting parameters are very crucial to learning results and generalization ability of SVM. But empirical parameters...
Accurate classification of caller interactions within Interactive Voice Response systems would assist corporations to determine caller behavior within these telephony applications. This paper details the development of such a classification system for a pay beneficiary application. Fuzzy Inference Systems, Multi-Layer Perceptron, Support Vector Machine and ensemble of classifiers were developed. Accuracy,...
With the development and widely used of Internet and information technology, the Web has become one of the most important means to obtain information for people. According to the Web document classification and the theory of artificial neural network, a Web classification mining method based on classify support vector machine (SVM) is presented in this paper. The SVM network structure that used for...
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