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wjoyn

木虫 (正式写手)

查到了,不错,继续支持。
11楼2009-11-26 10:59:54
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bigmount

铁杆木虫 (正式写手)

哪位能帮忙查一下,谢谢了


小木虫(金币+0.5):给个红包,谢谢回帖交流
哪位能帮忙查一下,谢谢了!
哪位能帮忙查一下,谢谢了
Multi-view learning for high dimensional data classification
看看这个检索了吗?
12楼2009-11-26 11:44:07
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wakinfan

铁杆木虫 (著名写手)

★ ★ ★
小木虫(金币+0.5):给个红包,谢谢回帖交流
飞扬2282(金币+2,VIP+0):感谢提供~~ 11-26 14:31
引用回帖:
Originally posted by bigmount at 2009-11-26 11:44:
哪位能帮忙查一下,谢谢了!
哪位能帮忙查一下,谢谢了
Multi-view learning for high dimensional data classification
看看这个检索了吗?

已检,祝贺了。

Accession number:  20094712469304

Title:  Multi-view learning for high dimensional data classification

Authors:  Li, Kunlun1 ; Meng, Xiaoqian1 ; Cao, Zheng1 ; Sun, Xue1  

Author affiliation:  1  College of Electronic and Information Engineering, Hebei University, Baoding 071002, China


Corresponding author:  Li, K. (likunlun@hbu.edu.cn)  

Source title:  2009 Chinese Control and Decision Conference, CCDC 2009

Abbreviated source title:  Chin. Control Decis. Conf., CCDC

Monograph title:  2009 Chinese Control and Decision Conference, CCDC 2009

Issue date:  2009

Publication year:  2009

Pages:  3766-3770

Article number:  5191691

Language:  English

ISBN-13:  9781424427239

Document type:  Conference article (CA)

Conference name:  2009 Chinese Control and Decision Conference, CCDC 2009

Conference date:  June 17, 2009 - June 19, 2009

Conference location:  Guilin, China

Conference code:  78484

Publisher:  IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States

Abstract:  Facing to the high dimensional data, how to deal them well is the most difficult problem in the field of machine learning, pattern recognition and the relative fields. In this paper, we propose a new semi-supervised multi-view learning method, which partition or select the abundant attributes (called attribute partition or attribute selection) into subsets. We consider each subset as a view and on each subset train a classifier to label the unlabeled examples. Based on the ensemble learning, we combine their predictions to classify the unlabeled examples. The semi-supervised learning idea is that to make use of the large number unlabeled example to modify the classifiers iteratively. Experiments on UCI datasets show that this method is feasible and can improve the efficiency. Both theoretical analysis and experiments show that the proposed method has excellent accuracy and speed of classification. © 2009 IEEE.

Number of references:  12
女口果人尔能看日月白这段言舌,那言兑日月人尔白勺目艮目青有严重白勺散光。
13楼2009-11-26 13:47:40
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wakinfan

铁杆木虫 (著名写手)

引用回帖:
Originally posted by bigmount at 2009-11-26 11:44:
哪位能帮忙查一下,谢谢了!
哪位能帮忙查一下,谢谢了
Multi-view learning for high dimensional data classification
看看这个检索了吗?

已检,祝贺!

刚发重了,不好意思,

[ Last edited by wakinfan on 2009-11-26 at 13:50 ]
女口果人尔能看日月白这段言舌,那言兑日月人尔白勺目艮目青有严重白勺散光。
14楼2009-11-26 13:48:25
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visitor958

至尊木虫 (文坛精英)

IEEE杂志与会议专家

这两个星期Ei检索的很多。。。
15楼2009-11-26 13:53:33
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jshxlxlw

木虫 (初入文坛)

请问是不是只检索英文?


小木虫(金币+0.5):给个红包,谢谢回帖交流
请问是不是只检索英文?
16楼2009-11-26 14:29:59
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xiaowei8771

新虫 (正式写手)

好啊,呵呵,我查到我的了,呵呵!!!
17楼2009-11-26 17:05:31
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恭喜恭喜。。。。。
18楼2009-11-26 18:17:29
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bigmount

铁杆木虫 (正式写手)

谢谢了!


小木虫(金币+0.5):给个红包,谢谢回帖交流
谢谢 wakinfan  了!
真的很感谢!
19楼2009-11-26 20:09:01
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zuoxg88

木虫 (小有名气)

请问怎么查啊?
20楼2009-11-26 20:56:14
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