Browsing by Author "Hung, Wen-Liang"

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A generalized possibilistic approach to shell clustering of template-based shapes
(2017-02)
Wang, Tsaipei
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Hung, Wen-Liang
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資訊工程學系
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Department of Computer Science
Efficiently and effectively detecting shell-like structures of particular shapes is an important task in computer vision and image processing. This paper presents a generalized possibilistic c-means algorithm (PCM) for shell clustering based on the diversity index of degree-lambda proposed by Patil and Taillie [Diversity as a concept and its measurement. J Amer Statist Assoc. 1982;77:548-561]. Experiments on various data sets in Wang [Possibilistic shell clustering of template-based shapes. IEEE Trans Fuzzy Syst. 2009;17:777-793] show that the the proposed generalized PCM performs better than Wang\'s [Possibilistic shell clustering of template-based shapes. IEEE Trans Fuzzy Syst. 2009;17:777-793] possibilistic shell clustering method according two two criteria: (i) the \'grade of detection\' g(d) for each target cluster; (ii) the amount of computation, denoted as k(c), required to attain a given g(d).
Weighted bootstrap for neural model selection
(2008)
Chuang, Shun-Chin
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Hung, Wen-Liang
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Fu, Hsin-Chia
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資訊工程學系
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Department of Computer Science
This article proposes a weighted bootstrap procedure, which is an efficient bootstrap technique for neural model selection. Our primary interest in reducing computer effort is to not resample (in the original bootstrap procedure) uniformly from the original sample, but to modify this distribution in order to obtain variance reduction. The performance of the weighted bootstrap is demonstrated on two artificial data sets and one real dataset. Experimental results show that the weighted bootstrap procedure permits an approximately 2 to 1 reduction in replication size.