AdaBoost algorithm based on fitted weak classifier
AdaBoost algorithm was proposed to minimize the accuracy caused by weak classifiers by minimizing the training error rate,and the single threshold was weaker and difficult to converge.The AdaBoost algorithm based on the fitted weak classifier was proposed.Firstly,the mapping relationship between eig...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2019-11-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019219/ |
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| _version_ | 1841530585564053504 |
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| author | Pengfeng SONG Qingwei YE Zhihua LU Yu ZHOU |
| author_facet | Pengfeng SONG Qingwei YE Zhihua LU Yu ZHOU |
| author_sort | Pengfeng SONG |
| collection | DOAJ |
| description | AdaBoost algorithm was proposed to minimize the accuracy caused by weak classifiers by minimizing the training error rate,and the single threshold was weaker and difficult to converge.The AdaBoost algorithm based on the fitted weak classifier was proposed.Firstly,the mapping relationship between eigenvalues and marker values was established.The least squares method was introduced to solve the fitting polynomial function,and the continuous fitting values were converted into discrete categorical values,thereby obtaining a weak classifier.From the many classifiers obtained,the classifier with smaller fitting error was selected as the weak classifier to form a new AdaBoost strong classifier.The UCI dataset and the MIT face image database were selected for experimental verification.Compared with the traditional Discrete-AdaBoost algorithm,the training speed of the improved algorithm was increased by an order of magnitude.And the face detection rate can reach 96.59%. |
| format | Article |
| id | doaj-art-46c81a997cc748cc806b8c2d7aa52fc8 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2019-11-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-46c81a997cc748cc806b8c2d7aa52fc82025-01-15T03:02:00ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012019-11-0135273559585915AdaBoost algorithm based on fitted weak classifierPengfeng SONGQingwei YEZhihua LUYu ZHOUAdaBoost algorithm was proposed to minimize the accuracy caused by weak classifiers by minimizing the training error rate,and the single threshold was weaker and difficult to converge.The AdaBoost algorithm based on the fitted weak classifier was proposed.Firstly,the mapping relationship between eigenvalues and marker values was established.The least squares method was introduced to solve the fitting polynomial function,and the continuous fitting values were converted into discrete categorical values,thereby obtaining a weak classifier.From the many classifiers obtained,the classifier with smaller fitting error was selected as the weak classifier to form a new AdaBoost strong classifier.The UCI dataset and the MIT face image database were selected for experimental verification.Compared with the traditional Discrete-AdaBoost algorithm,the training speed of the improved algorithm was increased by an order of magnitude.And the face detection rate can reach 96.59%.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019219/AdaBoostfitting typeleast squaresweak classifier |
| spellingShingle | Pengfeng SONG Qingwei YE Zhihua LU Yu ZHOU AdaBoost algorithm based on fitted weak classifier Dianxin kexue AdaBoost fitting type least squares weak classifier |
| title | AdaBoost algorithm based on fitted weak classifier |
| title_full | AdaBoost algorithm based on fitted weak classifier |
| title_fullStr | AdaBoost algorithm based on fitted weak classifier |
| title_full_unstemmed | AdaBoost algorithm based on fitted weak classifier |
| title_short | AdaBoost algorithm based on fitted weak classifier |
| title_sort | adaboost algorithm based on fitted weak classifier |
| topic | AdaBoost fitting type least squares weak classifier |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019219/ |
| work_keys_str_mv | AT pengfengsong adaboostalgorithmbasedonfittedweakclassifier AT qingweiye adaboostalgorithmbasedonfittedweakclassifier AT zhihualu adaboostalgorithmbasedonfittedweakclassifier AT yuzhou adaboostalgorithmbasedonfittedweakclassifier |