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Improved Extreme Learning Machine and Its Application in Image Quality Assessment

文献类型： 外文期刊

作者： Mao, Li 1 ; Zhang, Lidong 1 ; Liu, Xingyang 1 ; Li, Chaofeng 1 ; Yang, Hong 2 ;

作者机构： 1.Jiangnan Univ, Sch Internet ofThings, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Jiangsu, Peoples R China

2.Chinese Acad Fishery Sci, Freshwater Fisheries Res Ctr, Wuxi 214081, Jiangsu, Peoples R China

期刊名称：MATHEMATICAL PROBLEMS IN ENGINEERING （ 影响因子：1.305； 五年影响因子：1.27 ）

ISSN： 1024-123X

年卷期： 2014 年

页码：

收录情况： SCI

摘要： Extreme learning machine (ELM) is a new class of single-hidden layer feedforward neural network (SLFN), which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, whichmaintains the advantages fromoriginal ELM. However, the current reported ELMand its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting troubles, in this paper, we incorporate the structural risk minimization principle into the (weighted) ELM, and propose a modified (weighted) extreme learning machine (M-ELM and M-WELM). Experimental results show that our proposed M-WELM outperforms the current reported extreme learning machine algorithm in image quality assessment.

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