Restricted Boltzmann machine to determine the input weights for extreme learning machines

TL;DR

This paper introduces RBM-ELM, a novel method using restricted Boltzmann machines to determine input weights for Extreme Learning Machines, leading to improved performance over traditional random weight assignment.

Contribution

The paper proposes a new approach to set input weights in ELM using RBMs, enhancing effectiveness compared to existing methods.

Findings

RBM-ELM outperforms traditional ELM in accuracy

RBM-ELM surpasses existing weight selection algorithms

Improved learning performance demonstrated on benchmark datasets

Abstract

The Extreme Learning Machine (ELM) is a single-hidden layer feedforward neural network (SLFN) learning algorithm that can learn effectively and quickly. The ELM training phase assigns the input weights and bias randomly and does not change them in the whole process. Although the network works well, the random weights in the input layer can make the algorithm less effective and impact on its performance. Therefore, we propose a new approach to determine the input weights and bias for the ELM using the restricted Boltzmann machine (RBM), which we call RBM-ELM. We compare our new approach with a well-known approach to improve the ELM and a state of the art algorithm to select the weights for the ELM. The results show that the RBM-ELM outperforms both methodologies and achieve a better performance than the ELM.