Amaury Lendasse

TL;DR: The proposed OP-ELM methodology performs several orders of magnitude faster than the other algorithms used in this brief, except the original ELM, and is still able to maintain an accuracy that is comparable to the performance of the SVM.

TL;DR: This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation.

TL;DR: A global input selection strategy that combines forward selection, backward elimination (or pruning) and forward-backward selection is introduced and is used to optimize the three input selection criteria (k-NN, MI and NNE).

TL;DR: This paper presents a complete approach to a successful utilization of a high-performance extreme learning machines (ELM) Toolbox for Big Data, and summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance achievements.

TL;DR: The mutual information measures the information content in input variables with respect to the model output, without making any assumption on the model that will be used; it is suitable for nonlinear modelling and allows therefore a greater interpretability of the results.