融合极限学习机  

Fusion of Extreme Learning Machines

作　　者：张文博[1] 姬红兵[1] 

机构地区：[1]西安电子科技大学电子工程学院,西安710071

出　　处：《电子与信息学报》2013年第11期2728-2732,共5页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(60871074)资助课题

摘　　要：为提高极限学习机(Extreme Learning Machine,ELM)的分类性能,同时保留其训练速度快的优点,该文提出融合ELM的方法,详细分析了特征级融合及决策级融合两种实现方式。为实现决策级融合ELM,提出概率极限学习机(Probabilistic ELM,PELM),将传统ELM的数值型输出转化为概率型输出,使得不同特征的判决结果统一在固定范围。在此基础上,采用自适应权值的方式实现决策级融合,该方法充分考虑了分类器针对不同特征的判决准确率差异,无需先验知识及主观定义。实验证明,该文提出的融合ELM相较于传统的单一特征支持向量机(SVM)方法及ELM方法,具有更优的分类性能;在训练时间方面,优于SVM方法。In order to improve the classification performance of Extreme Learning Machine （ELM） and retain its advantage of the training speed, after a detailed analysis of feature level fusion and decision level fusion, fusion of ELM is proposed. To implement decision level fusion ELM, Probabilistic ELM （PELM） is proposed, which transforms the numeric outputs of ELM to the probabilistic outputs and unifies the outputs in a fixed range. On this basis, an adaptive weighted feature fusion method is introduced, which considers fully the difference accuracy rates of different features without the prior knowledge and subjective definition. Simulation experiments verify the correctness and the validity of the method, thus achieving a higher recognition rate compared to the Support Vector Machine （SVM） and ELM, and a good performace in terms of training time.

关 键 词：模式识别 极限学习机 数据融合 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]