Incremental threshold learning for classifier selection
文献类型：期刊论文
| 作者 | Pang, Yanwei2; Deng, Junping2; Yuan, Yuan1
|
| 刊名 | neurocomputing
 |
| 出版日期 | 2012-07-15 |
| 卷号 | 89页码:89-95 |
| 关键词 | Incremental learning Threshold-based classifier Classifier fusion Object detection Pattern recognition |
| ISSN号 | 0925-2312 |
| 产权排序 | 2 |
| 合作状况 | 国内 |
| 中文摘要 | threshold-based classifier is a simple yet powerful pattern classification tool, which has been frequently used in applications of object detection and recognition. a threshold-based classifier is usually associated with a unique one-dimensional feature. a properly selected threshold and a binary sign corresponding to the feature govern the classifier. however, the learning process is usually done in a batch manner. the batch algorithms are not suitable for sequentially incoming data because of the limitation of storage and prohibitive computation cost. to deal with sequentially incoming data, this paper proposes an incremental algorithm for incrementally learning the threshold-based classifiers. the proposed method can not only incrementally model the features but also estimate the threshold and training error in a close form. the effectiveness of the proposed algorithm is evaluated in the applications of gender recognition, face detection, and human detection. (c) 2012 elsevier b.v. all rights reserved. |
| 英文摘要 | threshold-based classifier is a simple yet powerful pattern classification tool, which has been frequently used in applications of object detection and recognition. a threshold-based classifier is usually associated with a unique one-dimensional feature. a properly selected threshold and a binary sign corresponding to the feature govern the classifier. however, the learning process is usually done in a batch manner. the batch algorithms are not suitable for sequentially incoming data because of the limitation of storage and prohibitive computation cost. to deal with sequentially incoming data, this paper proposes an incremental algorithm for incrementally learning the threshold-based classifiers. the proposed method can not only incrementally model the features but also estimate the threshold and training error in a close form. the effectiveness of the proposed algorithm is evaluated in the applications of gender recognition, face detection, and human detection. (c) 2012 elsevier b.v. all rights reserved. |
| WOS标题词 | science & technology ; technology |
| 学科主题 | computer science ; artificial intelligence |
| 类目[WOS] | computer science, artificial intelligence |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | local binary patterns ; face-recognition ; neural-network ; propagation ; scale |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000304638500009 |
| 公开日期 | 2012-09-03 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/20259]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China 2.Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pang, Yanwei,Deng, Junping,Yuan, Yuan. Incremental threshold learning for classifier selection[J]. neurocomputing,2012,89:89-95. |
| APA | Pang, Yanwei,Deng, Junping,&Yuan, Yuan.(2012).Incremental threshold learning for classifier selection.neurocomputing,89,89-95. |
| MLA | Pang, Yanwei,et al."Incremental threshold learning for classifier selection".neurocomputing 89(2012):89-95. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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