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- W1997501162 abstract "An important factor that influences the performance of support vector machine is how to select its parameters. In traditional C-support vector machine, it is difficult to select penalty parameter C and kernel parameters, inappropriate choice of those values may cause deterioration of its performance and increase algorithm complexity. In order to solve those problems, in this paper, selected v - support vector machine as the research object, proposed an optimal parameters search method for the Gaussian kernel v - support vector machine based on improved particle swarm optimization, constructed a non- specific person and isolated words speech recognition system based on v - support vector machine using the optimized parameters firstly. Experiments show that this new v - support vector machine method achieves better speech recognition correct rates than traditional C-support vector machine in different signal to noise ratios and different words, this new improved method of optimizing v - SVM parameters is very efficient and has shorter convergence time, and makes v - support vector machine have better Performance in speech recognition system." @default.
- W1997501162 created "2016-06-24" @default.
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- W1997501162 date "2013-01-01" @default.
- W1997501162 modified "2023-09-26" @default.
- W1997501162 title "A Parameters Optimization Method of v-Support Vector Machine and Its Application in Speech Recognition" @default.
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- W1997501162 doi "https://doi.org/10.4304/jcp.8.1.113-120" @default.
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