Meta-ELM: ELM with ELM hidden nodes

Abstract

Extreme Leaning Machine (ELM) simply randomly assigns input weights and biases, ineluctably leading to certain stochastic behaviors and reducing generalization performance. In this paper, we propose a meta-learning model of ELM, called Meta-ELM. The Meta-ELM architecture consists of several base ELMs and one top ELM. Therefore, the Meta-ELM learning proceeds in two stages. First, each base ELM is trained on a subset of the training data. Then, the top ELM is learned with the base ELMs as hidden nodes. Theoretical analysis and experimental results on a few artificial and benchmark regression datasets show that the proposed Meta-ELM model is feasible and effective.