Abstract
Model optimization namely the kernel function and parameter selection is an important factor to affect the generalization ability of support vector machine (SVM). To solve model optimization problem of support vector machine classifier, a novel algorithm (GC-ABC) is proposed which integrate artificial bee colony algorithm, greedy algorithm and chaos search strategy. The simulation results show that the accuracy of SVM optimized by GC-ABC is superior to the SVM optimized by genetic algorithm and ant colony algorithm. The experiments further suggest that GC-ABC algorithm has fast convergence and strong global search ability, which improves the performance of the support vector machine.
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Li, L. (2013). A Novel Algorithm for Kernel Optimization of Support Vector Machine. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_12
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DOI: https://doi.org/10.1007/978-3-642-38715-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38714-2
Online ISBN: 978-3-642-38715-9
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