



@article {2009,
	title = {Extreme learning machines for regression based on V-matrix method},
	journal = {Cognitive Neurodynamics},
	year = {2017},
	month = {2017/06/10},
	abstract = {<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>},
	issn = {1871-4099},
	doi = {10.1007/s11571-017-9444-2},
	url = {http://dx.doi.org/10.1007/s11571-017-9444-2},
	author = {Yang, Zhiyong and Zhang, Taohong and Lu, Jingcheng and Yuan Su and Zhang, Dezheng and Duan, Yaowu}
}
