... 人检测的SVM 核函数,在训练数据和应用序以及 工作平台相同的情况下,分别对2 种常见的SVM核函数即线性核函数(Linear Kernel)、高斯径向基函数 (RBF)进行了学习训练,最后得到了2 种不同的SVM 训练模型.
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不同分类核函数的相互比较分析表明，线性核函数最适合黄瓜病害识别。
The comparison of different kernel functions for SVM shows that liner kernel function is most suitable for recognition of cucumber disease.
不同分类核函数的相互比较分析表明，线性核函数最适于植物病害的分类识别。
The comparison of different kernel functions for SVM shows that liner kernel function is most suitable for shape recognition of plant disease spot.
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。
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.
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