[1]Cortes C, Vapnik V. Supportvector networks[J]. Machine Learning, 1995, 20(3):273293.
[2]Cristianini N, ShaweTaylor J. An introduction to support vector machines and other kernelbased learning methods[M]. Cambridge:Cambridge University Press, 2000.
[3]Wang Xibin,Zhang Xiaoping,Wang Hanhu.Parameter optimization of support vector machine and application based on particle swarm optimization mode search[J]. Journal of Computer Applications, 2011,31(12):33023304. (in Chinese)
[4]Hu Yunyan, Peng Minfang, Tian Chenglai, et al. Analog circuit fault diagnosis based on particle swarm optimization SVM [J]. Application Research of Computers, 2012, 29(11):40534054. (in Chinese)
[5]Guo Fengyi, Guo Changna, Wang Aijun, et al. The forecast model of mine water discharge based on particle swarm optimization and support vector machines[J]. Computer Engineering & Science, 2012,34(7):177181. (in Chinese)
[6]Wu Jinglong, Yang Shuxia, Liu Chengshui. Parameter selection for support vector machines based on genetic algorithms to shortterm power load forecasting[J]. Journal of Central South University (Science and Technology), 2009,40(1):180184. (in Chinese)
[7]Dai Shangping,Song Yongdong.Parameter selection of support vector machines based on the fusion of genetic algorithm and the particle swarm optimization[J]. Computer Engineering & Science, 2012,34(10):113117. (in Chinese)
[8]Huang Yongqing, Liang Changyong, Zhang Xiangde. Parameter establishment of an ant system based on uniform design[J]. Control and Decision,2006,21(1):9396.(in Chinese)
[9]UC irvine machine learning repository[EB/OL]. [ 20130620]. http://archive.ics.uci.edu/ml/.
附中文参考文献:
[3]王喜宾, 张小平, 王翰虎. 基于粒子群优化模式搜索的支持向量机参数优化及应用[J]. 计算机应用, 2011,31(12):33023304.
[4]胡云艳, 彭敏放, 田成来, 等. 基于粒子群算法优化支持向量机的模拟电路诊断[J]. 计算机应用研究, 2012, 29(11):40534054.
[5]郭凤仪, 郭长娜, 王爱军, 等. 基于粒子群优化支持向量机的煤矿水位预测模型[J]. 计算机工程与科学, 2012,34(7):177181.
[6]吴景龙, 杨淑霞, 刘承水. 基于遗传算法优化参数的支持向量机短期负荷预测方法[J]. 中南大学学报(自然科学版), 2009,40(1):180184.
[7]戴上平, 宋永东. 基于遗传算法与粒子群算法的支持向量机参数选择[J]. 计算机工程与科学, 2012,343(10):113117.
[8]黄永青, 梁昌勇, 张祥德. 基于均匀设计的蚁群算法参数设定[J]. 控制与决策, 2006, 21(1):9396. |