A parallel inertial S-iteration forward-backward algorithm for regression and classification problems

Abstract

In this paper, a novel algorithm, called parallel inertial S-iteration forward-backward algorithm (PISFBA) isproposed for finding a common fixed point of a countable family of nonexpansive mappings and convergencebehavior of PISFBA is analyzed and discussed. As applications, we apply PISFBA to estimate the weight con-necting the hidden layer and output layer in a regularized extreme learning machine. Finally, the proposedlearning algorithm is applied to solve regression and data classification problems