<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, Self-adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) was proposed as a new class of learning algorithm for single-hidden layer feed forward neural network (SLFN). In order to achieve good generalization performance, SaDE-ELM calculates the error on a subset of testing data for parameter optimization. Since SaDE-ELM employs extra data for validation to avoid the over fitting problem, more samples are needed for model training. In this paper, the cross-validation strategy is proposed to be embedded into the training phase so as to solve the overtraining problem. Experimental results demonstrate that the proposed algorithms are efficient for Facial Age Estimation.</p>
</abstract>

In this paper, Self-adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) was proposed as a new class of learning algorithm for single-hidden layer feed forward neural network (SLFN). In order to achieve good generalization performance, SaDE-ELM calculates the error on a subset of testing data for parameter optimization. Since SaDE-ELM employs extra data for validation to avoid the over fitting problem, more samples are needed for model training. In this paper, the cross-validation strategy is proposed to be embedded into the training phase so as to solve the overtraining problem. Experimental results demonstrate that the proposed algorithms are efficient for Facial Age Estimation.

Self-adaptive Differential Evolutionary Extreme Learning Machine and Its Application in Facial Age Estimation

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper, Self-adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) was proposed as a new class of learning algorithm for single-hidden...

Self-adaptive Differential Evolutionary Extreme Learning Machine and Its Application in Facial Age Estimation

Data publikacji: 11 kwi 2018

Zakres stron: 72 - 77

DOI: https://doi.org/10.21307/ijanmc-2017-031

Słowa kluczowe
Extreme learning machines, Differential evolution extreme learning machines, Self-adaptive differential evolution extreme learning machines, Facial age estimation

© 2017 Junhua Ku et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Self-adaptive Differential Evolutionary Extreme Learning Machine and Its Application in Facial Age Estimation

Data publikacji: 11 kwi 2018

Zakres stron: 72 - 77

DOI: https://doi.org/10.21307/ijanmc-2017-031

Słowa kluczoweExtreme learning machines, Differential evolution extreme learning machines, Self-adaptive differential evolution extreme learning machines, Facial age estimation

© 2017 Junhua Ku et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Extreme learning machines, Differential evolution extreme learning machines, Self-adaptive differential evolution extreme learning machines, Facial age estimation