计算机工程2019，Vol.45Issue(2)：173-177,5.DOI:10.19678/j.issn.1000-3428.0048910
基于Lasso稀疏学习的径向基函数神经网络模型
Radial Basis Function Neural Network Model Based on Lasso Sparse Learning
摘要
Abstract
The traditional Radial Basis Function (RBF) neural network model uses all hidden layer nodes to construct the model. In this case, the generalization performance of traditional RBF neural network model is degraded because of the lackness of the effective hidden layer nodes extraction mechanism, which will easily leads to more complicated of model.In order to solve this problem, this paper proposes an improved RBF neural network model. It realizes sparse representation of hidden layer nodes and output layer connection weights by Lasso sparse constraint to remove redundant and uncorrelated hidden layer nodes, and retain important hidden layer nodes. The shrinkage parameter are determined by Cross Validation (CV) and grid search, so as to optimize model classification performance. Experimental results show that RBF neural network model based on Lasso sparse learning can not only reduce the calculation complexity of the model, but also improve the classification accuracy compared with existing RBF neural network model.关键词
数据挖掘/Lasso稀疏学习/径向基函数/神经网络/收缩参数Key words
data mining/Lasso sparse learning/Radial Basis Function (RBF)/neural network/shrinkage parameter分类
信息技术与安全科学引用本文复制引用
崔晨,邓赵红,王士同..基于Lasso稀疏学习的径向基函数神经网络模型[J].计算机工程,2019,45(2):173-177,5.基金项目
国家自然科学基金 (61772239, 61403247) （61772239, 61403247）
国家重点研发计划 (2016YFB0800803) （2016YFB0800803）
江苏省杰出青年基金 (BK20140001). （BK20140001）
