基于线性组合核函数支持向量机的病害图像识别研究

Research on Plant Disease Recognition Based on Linear Combination of the Kernel Function Support Vector Machine

合理的选择、设计核函数是支持向量机方法的重要部分,不同的核函数代表了利用支持向量机解决非线性分类问题时,进行的不同的非线性映射。核函数使支持向量机可以很容易地实现非线性算法。为此,提出了一种新的核函数—线性组合核函数,将该核函数应用于支持向量机方法中,并使用该方法对北京地区甜瓜病害图像进行了识别分类;同时也与人工神经网络和其它经典支持向量机核函数的分类结果进行了对比,实验结果也验证了该核函数的有效性。

Reasonably choice and design kernel function if an important part of support vector machine, the different kernels function represent the different non-linear mapping that using support vector machine to solve nonlinear classification problems. So kernel function support vector Machine which is easy to achieve nonlinear algorithm. This paper presents a new kernel function-linear combination of the kernel function support vector machines. Beijing muskmelon and the use of the method for the identification of disease image classification; It also has something to do with artificial neural networks and other classic kernel support vector machine classification results are compared the experimental results have verified the effectiveness of the Kernel function.