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An ACO-based algorithm for parameter optimization of support vector machines
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DOI：10.1016/j.eswa.2010.03.067.png)
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One of the significant research problems in support vector machines (SVM) is the selection of optimal parameters that can establish an efficient SVM so as to attain desired output with an acceptable level of accuracy. The present study adopts ant colony optimization (ACO) algorithm to develop a novel ACO-SVM model to solve this problem. The proposed algorithm is applied on some real world benchmark datasets to validate the feasibility and efficiency, which shows that the new ACO-SVM model can yield promising results. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Ant colony optimization (ACO) algorithm
Support vector machines (SVM)
Parameter optimization
ACO-SVM model
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7.5
论文数:
2.9W
被引数:
10.2W
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A hybrid approach for feature subset selection using neural networks and ant colony optimization使用神经网络和蚁群优化的特征子集选择的混合方法
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