利用集成支撑矢量机提高分类性能  

Improvement classification performance by the support vector machine ensemble

作　　者：李青[1] 焦李成[1] 

机构地区：[1]西安电子科技大学智能信息处理研究所,陕西西安710071

出　　处：《西安电子科技大学学报》2007年第1期68-70,105,共4页Journal of Xidian University

基　　金：国家自然科学基金(60372050;60133010);国家863计划(2002AA135080)

摘　　要：为了提高支撑矢量机的泛化性能,利用l倍交叉筛选和控制样本特征属性策略建立了集成支撑矢量机,该集成策略加强了子分类器之间的互异性,进一步提高了集成学习机的分类性能,提高了学习机的泛化性能,同时具有较好的鲁棒性.Ensemble Methods are learning algorithms that construct a collection of individual classifiers which are independent and yet accurate, and then classify a new data point by taking vote of their predictions. The support Vector Machine （SVM） presents excellent performance in solving the problems with a small number of simple, nonlinear and local minima. The combination of the Support Vector Machine with Ensemble methods has been done by Hyun-Chul Kim based on the bagging algorithm, yet it does not show high robustness for its randomicity. In this paper, by a deep investigation into the principle of the SVM and the Ensemble Method, we propose two possible ways, cross validated committees and manipulating of the input feature strategies, to construct the SVM ensemble, which provides strong robustness according to experimental results.

关 键 词：集成方法 支撑矢量机 集成支撑矢量机 模式识别 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]