Abstract
As an important classification method, SVM has been widely used in different fields. But it is still a problem how to choose the favorable parameters of SVM. For optimizing the parameters and increasing the accuracy of SVM, this paper proposed an improved quantum behaved particle swarm algorithm based on a mutation operator (MQPSO). The new operator is used for enhancing the global search ability of particle. We test SVM based on MPSO method on solving the problem of image classification. Result shows our algorithm is quite stable and gets higher accuracy.
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Qian, Q., Gao, H., Wang, B. (2014). A SVM Method Trained by Improved Particle Swarm Optimization for Image Classification. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_27
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DOI: https://doi.org/10.1007/978-3-662-45646-0_27
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