| 支持向量机在柴油机尾气分析中的核模型选择 |
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| 引用本文：高阳,李国璋.支持向量机在柴油机尾气分析中的核模型选择[J].计算技术与自动化,2011,(1):114-118 |
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| 中文摘要:研究支持向量机（SVM）中常用核函数及其参数对分类效果的影响。在此基础上，联系柴油机尾气数据，应用交叉确认法（CV）得到在该数据集下拥有不同常用核函数的SVM的最优参数，及在最优参数下SVM的三个性能指标，即对训练集的交叉确认准确率、对测试集的分类准确率和寻优时间。对比各性能指标，结果表明：对于柴油机尾气数据，径向基核函数模型所对应的训练集交叉确认准确率最高，而其测试集分类准确率最低；线性核函数模型的寻优时间最短。综合考虑SVM的学习能力、外推能力及寻优时间，决定选择线性核函数作为SVM在柴油机尾气分析中的核模型。 |
| 中文关键词:支持向量机 尾气分析 核函数 柴油机 交叉确认法 |
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| The Nuclear Model Selection of SVM in the Analysis of Diesel Engine Exhaust Emissions |
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| Abstract:The effect of ordinary kernels and their parameters of the Support Vector Machine (SVM) on classification are researched. Then, use Cross-Validation (CV) to obtain the optimum parameters of SVM with different ordinary kernel on the diesel engine exhaust emissions data, and 3 performance indexes of SVM, the CV accuracy on training data and the classification accuracy on testing data as well as the parameter optimizing time, under the corresponding optimum parameters respectively. Comparing the same type indexes, the results are, for the diesel engine exhaust emissions data, the RBF kernel model has higher accuracy than other models at the first index, but the lower one at the second; the Linear kernel model takes shorter time on parameter optimizing than other ones. Considering the learning and extrapolating ability as well as the parameter optimizing time, linear kernel is determined to be used in SVM in the analysis of diesel engine exhaust emissions. |
| keywords:SVM analysis of exhaust emissions kernel diesel engine CV |
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