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Research on Least Squares Support Vector Machine Combinatorial Optimization Algorithm

机译:最小二乘支持向量机组合优化算法研究。

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摘要

LS-SVM(least squares support vector machine) has been widely used in engineering practice. However, the solving of LS-SVM still remains difficult under the condition of large sample. Based on algorithm of combinatorial optimization, this paper put forward the combinatorial optimization least squares support vector machine algorithm. On several different data aggregation of dimensions, the numerical value experiment and comparison are carried out on traditional LS-SVM algorithm, COLS-SVM algorithm and its improvement algorithm. The numerical value test has shown that COLS-SVM algorithm and Us improvement algorithm are effective and have certain advantages on time and regression accuracy, compared with traditional LS-SVM algorithm.
机译:LS-SVM(最小二乘支持向量机)已在工程实践中得到广泛应用。但是,在大样本情况下,仍然难以解决LS-SVM问题。在组合优化算法的基础上,提出了组合优化最小二乘支持向量机算法。在几个不同维度的数据集合上,对传统的LS-SVM算法,COLS-SVM算法及其改进算法进行了数值实验和比较。数值测试表明,与传统的LS-SVM算法相比,COLS-SVM算法和Us改进算法有效,在时间和回归精度上具有一定优势。

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