Probability based voting extreme learning machine for multiclass XML documents classification
Probability based voting extreme learning machine for multiclass XML documents classification
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用于多类 XML 文档分类的基于概率的投票极限学习机
DOI：
10.1007/s11280-013-0230-8
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发表时间：
2014-09
影响因子：
3.7
通讯作者：
Qiao, Baiyou
中科院分区：
文献类型：
--
作者：
Zhao, Xiangguo;Bi, Xin;Qiao, Baiyou
本文为多类XML文档分类提供了基于极限学习机（ELM）的新颖解决方案。 ELM是具有非常快速学习能力的广义单隐藏层馈电网络（SLFN）。改进的向量D模型D
This paper presents a novel solution based on Extreme Learning Machine (ELM) for multiclass XML documents classification. ELM is a generalized Single-hidden Layer Feedforward Network (SLFN) with extremely fast learning capacity. An improved vector model DSVM (Distribution based Structured Vector Model) is proposed to represent XML documents with more structural information and more precise semantic information. The XML documents classifiers are conducted based on PV-ELM (Probablity based Voting ELM) with a postprocessing methodε-RCC (ε- Revoting of Confusing Classes) to refine the voting results. To evaluate the overall performance of this solution, a series of experiments are conducted on two real datasets of news feeds online. The experimental results show that DSVM represents the XML documents more effectively and PV-ELM withε-RCC achieves a higher accuracy than original ELM algorithm for multiclass classification.
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DOI：
10.1016/j.engappai.2010.06.009
发表时间：
2010-10-01
影响因子：
8
作者：
Suresh, S.;Saraswathi, S.;Sundararajan, N.
通讯作者：
Sundararajan, N.
DOI：
10.1109/ijcnn.2008.4634028
发表时间：
2008-06
期刊：
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
影响因子：
--
作者：
Hai-Jun Rong;G. Huang;Y. Ong
通讯作者：
Hai-Jun Rong;G. Huang;Y. Ong
影响因子：
6
作者：
Huang, Guang-Bin;Zhu, Qin-Yu;Siew, Chee-Kheong
通讯作者：
Siew, Chee-Kheong
DOI：
10.1109/34.273716
发表时间：
1994-01-01
影响因子：
23.6
作者：
HO, TK;HULL, JJ;SRIHARI, SN
通讯作者：
SRIHARI, SN
DOI：
10.1109/tsmcb.2008.2010506
发表时间：
2009-08-01
影响因子：
--
作者：
Rong, Hai-Jun;Huang, Guang-Bin;Saratchandran, P.
通讯作者：
Saratchandran, P.