Maximum entropy-type classification likelihood methods
碩士 === 中原大學 === 應用數學研究所 === 95 === In fuzzy cluster analysis , the fuzzy c-means (FCM) clustering algorithm is the best known and most used method. There are many generalized types of FCM. Some of them such as fuzzy classification maximum likelihood (FCML) induce to penalized fuzzy c-means (PFCM) ,...
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ndltd-TW-095CYCU55070142015-10-13T13:55:57Z http://ndltd.ncl.edu.tw/handle/65073862497665161959 Maximum entropy-type classification likelihood methods 最大熵聚類概似方法 Jing-Tang Juang 莊景棠 碩士 中原大學 應用數學研究所 95 In fuzzy cluster analysis , the fuzzy c-means (FCM) clustering algorithm is the best known and most used method. There are many generalized types of FCM. Some of them such as fuzzy classification maximum likelihood (FCML) induce to penalized fuzzy c-means (PFCM) , Maximum entropy classification (MEC) and alternative fuzzy c-means (AFCM) , will be studied in this thesis, then we can get better results. In this paper , we make the extension of the FCM , based on this class of fuzzy c-means clustering algorithm , we extend them by adding a regularization , the regularization is change by membership , we can derive a generalized type of fuzzy c-means clustering algorithms , called the maximum entropy clustering algorithm (MEC). By doing some numerical examples , for estimating the parameters of the normal mixtures , we find that MEC is more accuracy and effective then PFCM and FCM. Miin-Shen Yang 楊敏生 2007 學位論文 ; thesis 30 zh-TW |
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碩士 === 中原大學 === 應用數學研究所 === 95 === In fuzzy cluster analysis , the fuzzy c-means (FCM) clustering algorithm is the best known and most used method. There are many generalized types of FCM. Some of them such as fuzzy classification maximum likelihood (FCML) induce to penalized fuzzy c-means (PFCM) , Maximum entropy classification (MEC) and alternative fuzzy c-means (AFCM) , will be studied in this thesis, then we can get better results.
In this paper , we make the extension of the FCM , based on this class of fuzzy c-means clustering algorithm , we extend them by adding a regularization , the regularization is change by membership , we can derive a generalized type of fuzzy c-means clustering algorithms , called the maximum entropy clustering algorithm (MEC). By doing some numerical examples , for estimating the parameters of the normal mixtures , we find that MEC is more accuracy and effective then PFCM and
FCM.
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author2 |
Miin-Shen Yang |
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Miin-Shen Yang Jing-Tang Juang 莊景棠 |
author |
Jing-Tang Juang 莊景棠 |
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Jing-Tang Juang 莊景棠 Maximum entropy-type classification likelihood methods |
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Jing-Tang Juang |
title |
Maximum entropy-type classification likelihood methods |
title_short |
Maximum entropy-type classification likelihood methods |
title_full |
Maximum entropy-type classification likelihood methods |
title_fullStr |
Maximum entropy-type classification likelihood methods |
title_full_unstemmed |
Maximum entropy-type classification likelihood methods |
title_sort |
maximum entropy-type classification likelihood methods |
publishDate |
2007 |
url |
http://ndltd.ncl.edu.tw/handle/65073862497665161959 |
work_keys_str_mv |
AT jingtangjuang maximumentropytypeclassificationlikelihoodmethods AT zhuāngjǐngtáng maximumentropytypeclassificationlikelihoodmethods AT jingtangjuang zuìdàshāngjùlèigàishìfāngfǎ AT zhuāngjǐngtáng zuìdàshāngjùlèigàishìfāngfǎ |
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