引入调整项的模糊孪生支持向量机

Regularized Fuzzy Twin Support Vector Machine

模糊孪生支持向量机是一种重要的机器学习方法,克服了噪声或异常数据对分类的影响;然而,该方法考虑的仍是经验风险,从而使得训练过程易出现过拟合现象。为了解决该问题,通过引入调整项,提出了一种改进的模糊孪生支持向量机模型,利用二次规划求解方法和超松弛迭代法对模型进行求解,获得了用于分类的决策面。实验中选取UCI标准数据集验证了所提方法的有效性。

Fuzzy twin support vector machine is an important machine learning method and it overcomes the impact of noise and outlier data on classification.However,this method still accomplishes minimization of empirical risk so that overfitting is easily produced in the process of training.In order to solve this problem,a modified fuzzy twin support vector machine model was presented by introducing regularized item.Classifier was obtained by using quadratic programming and over-relaxation method to solve the model.Some UCI datasets were selected to conduct the experiments.The results validates the effectiveness of the proposed method.