Bayesian Regularized Committee of Extreme Learning Machine

摘要

Extreme Learning Machine (ELM) is an efficient learning algorithm for Single-Hidden Layer Feedforward Networks (SLFNs). Its main advantage is its computational speed due to a random initialization of the parameters of the hidden layer, and the subsequent use of Moore-Penrose's generalized inverse in order to compute the weights of the output layer. The main inconvenient of this technique is that as some parameters are randomly assigned and remain unchanged during the training process, they can be non-optimum and the network performance may be degraded. This paper aims to reduce this problem using ELM committees. The way to combine them is to use a Bayesian linear regression due to its advantages over other approaches. Simulations on different data sets have demonstrated that this algorithm generally outperforms the original ELM algorithm.