An experimental study on stability and generalization of extreme learning machines

Ai-Min Fu, Chunru Dong, Lai-Sheng Wang

Abstract: This paper gives an experimental study on the stability of an extreme learning machine (ELM) and its generalization capability. Focusing on the relationship between uncertainty of an ELM’s output on the training set and the ELM’s generalization capability, the experiments show some new results in the viewpoint of classical pattern recognition. The study provides some useful guidelines to choose a fraction of ELMs with better generalization from an ensemble for classification problems.