计算机科学与探索2019，Vol.13Issue(4)：657-665,9.DOI:10.3778/j.issn.1673-9418.1806015
一种粒子群优化的SVM-ELM模型*
SVM-ELM Model Based on Particle Swarm Optimization*
王丽娟 1丁世飞2
作者信息
- 1. 中国矿业大学 计算机科学与技术学院,江苏 徐州 221116
- 2. 徐州工业职业技术学院 信息与电气工程学院,江苏 徐州 221140
- 折叠
摘要
Abstract
Extreme learning machine (ELM) is a simple and effective SLFNs (single hidden layer feedforward neural networks) learning algorithm, in recent years has become one of the hot areas of machine learning research. But single hidden layer node lacks judgment to some extent. The classification accuracy depends on the number of hidden layer nodes. In order to improve the sense ability of single hidden layer node, the support vector machine (SVM) is combined with ELM, a simplified SVM-ELM model. At the same time, in order to avoid the subjectivity of human to choose parameters, using particle swarm optimization (PSO) algorithm to automatically select the parameters, finally PSO-SVM-ELM model is established. Experiments show that classification accuracy of the model is improved than the SVM-ELM and ELM, and the model also has good robustness and generalization.关键词
粒子群算法(PSO)/支持向量机(SVM)/极速学习机(ELM)/SVM-ELMKey words
particle swarm optimization (PSO)/ support vector machine (SVM)/ extreme learning machine (ELM)/SVM-ELM分类
信息技术与安全科学引用本文复制引用
王丽娟,丁世飞..一种粒子群优化的SVM-ELM模型*[J].计算机科学与探索,2019,13(4):657-665,9.
