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11楼2009-11-26 10:59:54

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bigmount

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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
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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
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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

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