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论文类型：期刊论文

发表时间：2017-05-01

发表刊物：JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY

收录刊物：SCIE、EI、CSCD、Scopus

卷号：32

期号：3

页面范围：630-643

ISSN号：1000-9000

关键字：single-hidden-layer feed-forward network (SLFN); incremental extreme learning machine (I-ELM); random hidden node; convergence rate; universal approximation

摘要：Extreme learning machine (ELM) is a learning algorithm for generalized single-hidden-layer feed-forward networks (SLFNs). In order to obtain a suitable network architecture, Incremental Extreme Learning Machine (I-ELM) is a sort of ELM constructing SLFNs by adding hidden nodes one by one. Although kinds of I-ELM-class algorithms were proposed to improve the convergence rate or to obtain minimal training error, they do not change the construction way of I-ELM or face the over-fitting risk. Making the testing error converge quickly and stably therefore becomes an important issue. In this paper, we proposed a new incremental ELM which is referred to as Length-Changeable Incremental Extreme Learning Machine (LCI-ELM). It allows more than one hidden node to be added to the network and the existing network will be regarded as a whole in output weights tuning. The output weights of newly added hidden nodes are determined using a partial error-minimizing method. We prove that an SLFN constructed using LCI-ELM has approximation capability on a universal compact input set as well as on a finite training set. Experimental results demonstrate that LCI-ELM achieves higher convergence rate as well as lower over-fitting risk than some competitive I-ELM-class algorithms.