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论文类型：期刊论文

发表时间：2014-02-01

发表刊物：JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE C-COMPUTERS & ELECTRONICS

收录刊物：EI、SCIE

卷号：15

期号：2

页面范围：119-125

ISSN号：1869-1951

关键字：Extreme learning machine (ELM); L-1/2 regularizer; Network pruning

摘要：Compared with traditional learning methods such as the back propagation (BP) method, extreme learning machine provides much faster learning speed and needs less human intervention, and thus has been widely used. In this paper we combine the L (1/2) regularization method with extreme learning machine to prune extreme learning machine. A variable learning coefficient is employed to prevent too large a learning increment. A numerical experiment demonstrates that a network pruned L (1/2) regularization has fewer hidden nodes but provides better performance than both the original network and the network pruned by L (2) regularization.