Boosting ridge for the extreme learning machine globally optimised for classification and regression problems.

ABSTRACT: This paper explores the boosting ridge (BR) framework in the extreme learning machine (ELM) community and presents a novel model that trains the base learners as a global ensemble. In the context of Extreme Learning Machine single-hidden-layer networks, the nodes in the hidden layer are preconfigured before training, and the optimisation is performed on the weights in the output layer. The previous implementation of the BR ensemble with ELM (BRELM) as base learners fix the nodes in the hidden layer for all the ELMs. The ensemble learning method generates different output layer coefficients by reducing the residual error of the ensemble sequentially as more base learners are added to the ensemble. As in other ensemble methodologies, base learners are selected until fulfilling ensemble crite

SUBMITTER: Peralez-Gonzalez C 

PROVIDER: S-EPMC10362034 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature