Abstract
Map** way plays a significant role in Support Vector Machine (SVM). An appropriate map** can make data distribution in higher dimensional space easily separable. In this paper Morlet-RBF kernel model is proposed. That is, Morlet wavelet kernel is firstly used to transform data, then Radial Basis Function (RBF)is used to map the already transformed data into another higher space. And particle swarm optimization (PSO) is applied to find best parameters in the new kernel. Morlet-RBF kernel is compared with Mexican-Hat wavelet kernel and RBF kernel. Experimental results show the feasibility and validity of this new map** way in classification of medical images.
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Jiang, H., Liu, X., Zhou, L., Fujita, H., Zhou, X. (2011). Morlet-RBF SVM Model for Medical Images Classification. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21090-7_14
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DOI: https://doi.org/10.1007/978-3-642-21090-7_14
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