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为解决支持向量机(SVM)参数选择问题,采用Cheby-shev映射、改变优化搜索公式和增加3次载波,提出一种改进的加速混沌优化算法(ISCOA).应用人工数据集和实际数据集进行论证,并与常规的交叉验证法(CV)进行比较.结果证明了该改进算法在支持向量机参数选择中的有效性和实用性.
In order to solve the problem of SVM parameter selection, an improved accelerated chaos optimization algorithm (ISCOA) is proposed by using Cheby-shev mapping, changing the optimal search formula and adding three carriers. The artificial dataset and the actual dataset Demonstration and comparison with the conventional cross-validation (CV) method.The results show the effectiveness and practicability of the improved algorithm in SVM parameter selection.