一种基于SVM新的学习方法及应用

A New Method for Support Vector Machine and Application

该文提出一种基于支持向量机的组合核函数的学习方法,它首先由遗传算法作为新的学习方法得到训练,组合核函数的权值在学习过程中被确定,并在决策模型的分类阶段用来作为参数。这种学习方法被应用在两个关于癌症诊断的公用数据集中,从而获得分类最优超平面。通过实验,这种学习方法显示出比用单一核函数具有较好的性能。

This paper proposes a unified kernel function for support vector machine, which are trained by a new learning method based on genetic algorithm.The weights of basis kernel functions in the unified kernel are determined in learning phase and used as the parameters in the decision model in the classification phase.The unified kernel and the learning method were applied to obtain the optimal decision model for the classification of two public data sets for diagnosis of cancer diseases.The experiment showed fast convergence and greater flexibility than other kernel functions.