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Your search keyword '"Chen, Qiong"' showing total 34 results
Start Over Author "Chen, Qiong" Publication Type Books
34 results on '"Chen, Qiong"'

14. Wide Complex Tachycardia

Author

Gianni, Carola, primary, Chen, Qiong, additional, Canpolat, Uğur, additional, Della Rocca, Domenico G., additional, Al-Ahmad, Amin, additional, Burkhardt, J. David, additional, and Natale, Andrea, additional

Published
2020
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33. Diversity Maintenance Strategy Based on Global Crowding.

Author

Chen, Qiong, Xiong, Shengwu, and Liu, Hongbing

Abstract

In the design of multi-objective evolutionary algorithm, the diversity maintenance is essential to access the convergence of multi-objective optimization solutions. This paper presents a new diversity maintenance strategy based on global crowding, which is addressed for pruning non-dominated solutions as well as preserving a wide-spread distributed solution set and maintaining population diversity. Later on, inspired by the conception of entropy in information theory, the entropy metrics is defined and applied to assess the proposed strategy. Two-dimensional and multi-dimensional numerical experiment results demonstrate that the proposed strategy shows better performance in the entropy reduction and losses of uniform distribution than traditional diversity maintenance strategies. [ABSTRACT FROM AUTHOR]

Published
2009
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34. Extraction of the Reduced Training Set Based on Rough Set in SVMs.

Author

Liu, Hongbing, Xiong, Shengwu, and Chen, Qiong

Abstract

In SVMs, the data points lying in the interactive regions of two classes are very important to form the hyperplane and difficult to be classified. How to select the reduced training set only including the interactive data points is one of the important issues. There are many methods by which the easy misclassified training data are selected to speed up training. The extraction method of the reduced training set is proposed by using the boundary of rough set. Firstly, for two-class problem, the entire training set is partitioned into three regions: the region only containing the positive samples, the region only composed of the negative samples and the boundary region including not only the positive samples but also the negative ones. Secondly, the boundary region is the intersection of two classes and selected to train SVMs. Thirdly, the two-class and multi-class problems are used to verify the feasibility of the proposed SVMs. The experimental results on the classic benchmark data set of machine learning show that the proposed learning machines can downsize the number of training data and hardly influence on their generalization abilities. [ABSTRACT FROM AUTHOR]

Published
2009
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