路径跟踪线性规划向量机

摘要

研究路径跟踪线性规划支持向量机(path following linear programming support vector machine,PF-LPSVM)分类算法,利用路径跟踪法求解线性规划的高效性,提高线性规划支持向量机在大规模数据集上的学习效率.给出线性规划支持向量机的模型并将其标准化,导出用路径跟踪法求解线性规划向量机的关键公式,给出完整的算法流程.在随机数据集及UCI数据集上,将所提算法与LibSVM和牛顿法线性规划向量机(Newton-LPSVM,N-LPSVM)做比较,实验结果表明,所提算法用路径跟踪法提高LPSVM的学习效率是可行的,其适用于大规模数据集的学习.%The path following linear programming support vector machine (PF-LPSVM)) classification algorithm was studied.The advantage of efficiency of the path following method in solving linear programming was used to improve the learning efficiency of LPSVM on large scale data sets.The model of linear programming support vector machine was given and standardized.The key formula of linear programming vector machine was derived using path following method,and the complete algorithm flow was given.On the random data set and UCI data set,the proposed algorithm was compared with LibSVM and Newton method linear programming vector machine (Newton-LPSVM,N-LPSVM).Experimental results show that the algorithm proposed is feasible to improve the learning efficiency of LPSVM using path following method,which is suitable for large-scale data sets.