Minxia Luo

TL;DR: Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency.

TL;DR: This work proposes an outlier-robust ELM where the l 1 -norm loss function is used to enhance the robustness and the fast and accurate augmented Lagrangian multiplier method is applied to guarantee the effectiveness and efficiency.

TL;DR: A novel hybrid approach combining ELM and SRC for image classification is proposed and results demonstrate that it not only outperforms ELM in classification accuracy but also has much less computational complexity than SRC.

TL;DR: An enhanced ensemble based ELM and SRC algorithm that incorporates multiple ensembles to enhance the reliability of the classifier and win a better classification performance with a lower computational complexity than the ELM-SRC approach.

TL;DR: A new full implication algorithm based on interval-valued fuzzy inference which extends the triple I principle for fuzzy inference based on fuzzymodus ponens and fuzzy modus tollens to the case of interval- valued fuzzy sets is presented.