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CLC number: TP312

On-line Access: 2014-01-29

Received: 2013-07-22

Revision Accepted: 2013-10-14

Crosschecked: 2014-01-15

Cited: 3

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.2 P.119-125


A pruning algorithm with L1/2 regularizer for extreme learning machine

Author(s):  Ye-tian Fan, Wei Wu, Wen-yu Yang, Qin-wei Fan, Jian Wang

Affiliation(s):  School of Mathematical Sciences, Dalian University of Technology, Dalian 116023, China; more

Corresponding email(s):   fanyetian@mail.dlut.edu.cn, wuweiw@dlut.edu.cn

Key Words:  Extreme learning machine (ELM), L1/2 regularizer, Network pruning

Ye-tian Fan, Wei Wu, Wen-yu Yang, Qin-wei Fan, Jian Wang. A pruning algorithm with L1/2 regularizer for extreme learning machine[J]. Journal of Zhejiang University Science C, 2014, 15(2): 119-125.

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author="Ye-tian Fan, Wei Wu, Wen-yu Yang, Qin-wei Fan, Jian Wang",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

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%T A pruning algorithm with L1/2 regularizer for extreme learning machine
%A Ye-tian Fan
%A Wei Wu
%A Wen-yu Yang
%A Qin-wei Fan
%A Jian Wang
%J Journal of Zhejiang University SCIENCE C
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300197

T1 - A pruning algorithm with L1/2 regularizer for extreme learning machine
A1 - Ye-tian Fan
A1 - Wei Wu
A1 - Wen-yu Yang
A1 - Qin-wei Fan
A1 - Jian Wang
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 2
SP - 119
EP - 125
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300197

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.


研究背景:1. 神经网络有着广泛的应用,但收敛速度慢、精度低,影响了它的发展。相较于传统的神经网络,极端学习机克服了这些缺点,它不仅提供更快的学习速度,而且只需较少的人工干预,这些优点使得极端学习机得到了广泛应用。2. 相比于L1和L2正则化,L1/2正则化的解具有更好的稀疏性;而与L0正则化相比,它又更容易求解。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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