一种模糊最小二乘孪生支持向量回归机的改进算法

摘要

模糊最小二乘孪生支持向量机模型融合了模糊函数和最小二乘孪生支持向量机算法特性,以解决训练数据集存在孤立点噪声和运算效率低下问题.针对回归过程基于统计学习结构风险最小化原则,对该模型进行L2范数正则化改进.考虑到大规模数据集的训练效率问题,对原始模型进行了L1范数正则化改进.基于增量学习特性,对数据集训练过程进行增量选择迭加以加快训练速度.在UCI数据集上验证了相关改进算法的优越性.%Fuzzy least squares twin support vector machines model combined the characteristics of fuzzy function and least squares twin support vector machines algorithm to solve the problem of isolated point noise and inefficiency of training data set.According to regression progress based on the principle of minimizing the risk of statistical learning structure,a regularization of L2 norm was improved for this model.Taking into account the training efficiency of largescale data sets,L1 model regularization was improved on the original model.Finally,based on the incremental learning characteristics,the data set training process was incrementally selected and superposed to speed up training.The superiority of the improved algorithm was verified on the UCI dataset.