The nature of statistical learning theory
The nature of statistical learning theory
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machine Training for Improved Hidden Markov Modeling
IEEE Transactions on Signal Processing
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Parameters selection of support vector machine is a very important problem, which has great influence on the performance of support vector machine. Particle swarm optimization is an efficient algorithm and it is broadly used in many research areas like pattern recognition and so on. In order to improve the learning and generalization ability of support vector machine, a method for searching the optimal parameters based on particle swarm optimization is proposed in this paper. We constructed a speech recognition system based on support vector machine using the optimal parameters. The kernel function we used is radial basis function and the speech data is isolated, non-specific and middle vocabulary words. The speech feature we used is MFCC feature. Experiments indicate that the accuracy of speech recognition is efficiently improved by using support vector machine of the optimal parameters, which has practicability to some extent. This method provides an efficient approach for searching for optimal parameters of support vector machine.