TY - JOUR T1 - Extreme learning machines for regression based on V-matrix method JF - Cognitive Neurodynamics Y1 - 2017 A1 - Yang, Zhiyong A1 - Zhang, Taohong A1 - Lu, Jingcheng A1 - Yuan Su A1 - Zhang, Dezheng A1 - Duan, Yaowu AB -

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.

UR - http://dx.doi.org/10.1007/s11571-017-9444-2 U5 - 10.1007/s11571-017-9444-2 ER -