



01360nas a2200181   4500008004100000022001400041245007000055210006900125260001500194520081800209100001801027700001901045700001801064700001301082700001901095700001601114856004801130       2017                        eng d  a1871-409900aExtreme learning machines for regression based on V-matrix method0 aExtreme learning machines for regression based on Vmatrix method  c2017/06/103 a<p>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.</p>1 aYang, Zhiyong1 aZhang, Taohong1 aLu, Jingcheng1 aSu, Yuan1 aZhang, Dezheng1 aDuan, Yaowu  uhttp://dx.doi.org/10.1007/s11571-017-9444-2