Comparative study of extreme learning machine and support vector machine

摘要: Comparative study of extreme learning machine (ELM) and support vector (SVM) is investigated in this paper. A cross validation method for determining the appropriate number neurons hidden layer also proposed ELM by Huang, et al [3] a novel machine-learning algorithm single hidden-layer feedforward neural network (SLFN), which randomly chooses input weights bias, analytically determines output optimally instead tuning them. This tends to produce good generalization ability obtain least experience risk simultaneously with solid foundations. Benchmark tests real Tennessee Eastman Process (TEP) are carried out validate its superiority. Compared SVM, much faster has better performance than SVM case studied