Abstract
Kernel selection is a main factor in the designing of support vector machines. Evolutionary techniques have been applied to select the fittest kernel for specific classification problems. However, technical issues emerge when attempting to apply this methodology to deal with large datasets. On the other hand, a new method for improving the training time of support vector machines was recently developed. In this chapter, the new method is integrated in a kernel evolution scheme. Ten benchmark datasets are tested. Results indicate that the new method speeds up the evolution process when datasets are greater than 1000 instances.
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Acknowledgments
Luis Carlos Padierna García wishes to acknowledge the financial support of the Consejo Nacional de Ciencia y Tecnología (CONACYT grant 375524). The authors also thank the support of the Tecnológico Nacional de México – Instituto Tecnológico de León.
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Padierna, L.C., Carpio, M., Baltazar, R., Puga, H.J., Fraire, H.J. (2015). Evolution of Kernels for Support Vector Machine Classification on Large Datasets. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_10
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DOI: https://doi.org/10.1007/978-3-319-17747-2_10
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