%0 Journal Article %J Cognitive Neurodynamics %D 2017 %T Extreme learning machines for regression based on V-matrix method %A Yang, Zhiyong %A Zhang, Taohong %A Lu, Jingcheng %A Yuan Su %A Zhang, Dezheng %A Duan, Yaowu %X

This paper studies the joint effect of V-matrix, a recently proposed framework for statistical inferences, and extreme learning machine (ELM) on regression problems. First of all, a novel algorithm is proposed to efficiently evaluate the V-matrix. Secondly, a novel weighted ELM algorithm called V-ELM is proposed based on the explicit kernel mapping of ELM and the V-matrix method. Though V-matrix method could capture the geometrical structure of training data, it tends to assign a higher weight to instance with smaller input value. In order to avoid this bias, a novel method called VI-ELM is proposed by minimizing both the regression error and the V-matrix weighted error simultaneously. Finally, experiment results on 12 real world benchmark datasets show the effectiveness of our proposed methods.

%B Cognitive Neurodynamics %8 2017/06/10 %G eng %U http://dx.doi.org/10.1007/s11571-017-9444-2 %R 10.1007/s11571-017-9444-2