支持向量机的研究

Research on the Application of Support Vector Machine

支持向量机可以引入特征变换将原空间的非线性问题转化为新空间的线性问题。本文在论述支持向量机模型创建的基础上,着重对核函数的选取及参数的确定进行了研究,通过实验数据表明,文中创建的组合核函数,在人体下肢动作模式识别中,有较高的识别率。

Support vector machine （ SVM） can convert the nonlinear problem of the original space into the linear problem of new space by introducing feature transform. Based on discussing the model creation of SVM, this paper mainly studies the selection of kernel function and the determination of parameters. The experimental data shows that the combined kernel function created in this paper has higher recognition rate in human lower limb motion pattern recognition.