聊城大学学报（自然科学版）2016，Vol.29Issue(3)：1-7,7.
四种TSVR型学习算法的性能比较
Performance Comparison for Four TSVR-type Learning Algorithms
摘要
Abstract
It is w ell know n that the computational complexity and sparsity of learning algorithms based on support vector regression machines (SVRs) are two main factors for analyzing and treating big data ,especially for high dimensional data .According to the two factors ,scholars did a lot of research work and proposed many improved SVR‐type learning algorithms .Among these improved algorithms , some have the basically same starting point ,just solving methods are slightly different ;some have dis‐tinctly different starting point and then result in different optimization problems ,but the solving meth‐ods are similar .For deep understanding these improved algorithms and being more selective in the appli‐cations ,this paper is devoted to analyze and compare the performance for four more representative TS‐VR‐type algorithms .关键词
孪生支持向量回归机/最小二乘/边界/参数不敏感Key words
twin support vector regression machine/least squares/boundary/parameter insensitive分类
数理科学引用本文复制引用
李艳蒙,范丽亚..四种TSVR型学习算法的性能比较[J].聊城大学学报（自然科学版）,2016,29(3):1-7,7.基金项目
国家自然科学基金项目（11501278）；山东省自然科学基金项目（ZR2013AQ011）资助 （）
