| A pruning algorithm with L1/2regularizer for extreme learning machine简 |
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| 作者姓名： | Ye-tian FAN Wei WU Wen-yu YANG Qin-wei FAN Jian WANG |
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| 基金项目： | Project supported by the National Natural Science Foundation of China （No. 11171367） and the Fundamental Research Funds for the Central Universities, China |
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| 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 L1/2 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 by L1/2 regularization has fewer hidden nodes but provides better performance than both the original network and the network pruned by L2 regularization.
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| 关 键 词： | Extreme learning machine （ELM） L1/2 regularizer Network pruning |
A pruning algorithm with L
1/2 regularizer for extreme learning machine |
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| Ye-tian FAN,Wei WU,Wen-yu YANG,Qin-wei FAN,Jian WANG.A pruning algorithm with L
1/2 regularizer for extreme learning machine[J].Journal of Zhejiang University-Science C(Computers and Electronics),2014,15(2):119-125. |
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| Authors: | Ye-tian Fan Wei Wu Wen-yu Yang Qin-wei Fan Jian Wang |
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| Affiliation: | 1. School of Mathematical Sciences, Dalian University of Technology, Dalian, 116023, China
2. College of Science, Huazhong Agricultural University, Wuhan, 430070, China
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| Abstract: | 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. |
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| Keywords: | Extreme learning machine(ELM) L/ regularizer Network pruning |
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