A fast and effective Extreme learning machine algorithm without tuning
Meng Joo Er, Zhifei Shao, Ning Wang · 2014
Artificial Neural Networks (ANN) is a major machine learning technique inspired by biological neural networks. However, the process of its parameter tuning is usually tedious and time consuming, and thus it becomes a major bottleneck for it being efficiently applied and used by nonexperts. In this paper, a novel ANN algorithm, termed as Automatic Regularized Extreme Learning Machine (AR-ELM), based on a Regularized Extreme Learning Machine (RELM) using ridge regression is proposed. It is a true automatic ANN learning algorithm in the sense that it can automatically identify the appropriate essential system parameter according to the input data without the need of user intervention. Since this method is based on a relatively straightforward formula, it can achieve very fast learning speed. The simulation results shows that the proposed AR-ELM algorithm can achieve comparable results to tedious cross-validation tuned RELM. Furthermore, we also systematically investigate one of the biggest concerns of ELM, its randomness nature, caused by randomly generated parameters.