自动化学报2011，Vol.37Issue(11)：1344-1350,7.DOI:10.3724/SP.J.1004.2011.01344
基于替代函数及贝叶斯框架的1范数ELM算法
An Norm 1 Regularization Term ELM Algorithm Based on Surrogate Function and Bayesian Framework
摘要
Abstract
Focusing on the ill-posed problem and the model scale control of ELM (Extreme learning machine), this paper proposes an improved ELM algorithm based on 1-norm regularization term. This is achieved by involving an 1-norm regularization term into the original square cost function, and it can be used to control the model scale and enhance the generalization capability. Furthermore, to simplify the solving process of the 1-norm regularization method, the bound optimization algorithm is employed and a suitable surrogate function is established. Based on the surrogate function, the Bayesian algorithm can be used to substitute the complicated cross validation method and estimate the regularization parameter adaptively. Simulation results illustrate that the proposed method can effectively simplify the model structure, while remaining acceptable prediction accurate.关键词
1范数正则化/极端学习机/替代函数/贝叶斯方法Key words
Norm 1 regularization/ extreme learning machine (ELM)/ surrogate function/ Bayesian method引用本文复制引用
韩敏,李德才..基于替代函数及贝叶斯框架的1范数ELM算法[J].自动化学报,2011,37(11):1344-1350,7.基金项目
国家自然科学基金(61074096)资助 （61074096）
