关键词: 粒子群算法（PSO）, 支持向量机（SVM）, 极速学习机（ELM）, SVM-ELM

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.

Key words: particle swarm optimization (PSO), support vector machine (SVM), extreme learning machine (ELM), SVM-ELM