CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Joint learning of error-correcting output codes and dichotomizers from data
Zhong, Guoqiang; Huang, Kaizhu; Liu, Cheng-Lin
Source PublicationNEURAL COMPUTING & APPLICATIONS
2012-06-01
Volume21Issue:4Pages:715-724
SubtypeArticle
AbstractThe ECOC technique is a powerful tool to learn and combine multiple binary learners for multi-class classification. It generally involves three steps: coding, dichotomizers learning, and decoding. In previous ECOC methods, the coding step and the dichotomizers learning step are usually performed independently. This simplifies the learning problem but may lead to unsatisfactory decoding results. To solve this problem, we propose a novel model for learning the ECOC matrix and dichotomizers jointly from data. We formulate the model as a nonlinear programming problem and develop an efficient alternating minimization algorithm to solve it. Specifically, for fixed ECOC matrix, our model is decomposed into a group of mutually independent quadratic programming problems; while for fixed dichotomizers, it is a difference of convex functions problem and can be easily solved using the concave--convex procedure algorithm. Our experimental results on ten data sets from the UCI machine learning repository demonstrated the advantage of our model over state-of-the-art ECOC methods.
KeywordError Correcting Output Codes (Ecoc) Dichotomizer Concave-convex Procedure (Cccp)
WOS HeadingsScience & Technology ; Technology
WOS KeywordCLASSIFICATION ; DESIGN
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000304160200011
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7994
Collection模式识别国家重点实验室_模式分析与学习
AffiliationChinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhong, Guoqiang,Huang, Kaizhu,Liu, Cheng-Lin. Joint learning of error-correcting output codes and dichotomizers from data[J]. NEURAL COMPUTING & APPLICATIONS,2012,21(4):715-724.
APA Zhong, Guoqiang,Huang, Kaizhu,&Liu, Cheng-Lin.(2012).Joint learning of error-correcting output codes and dichotomizers from data.NEURAL COMPUTING & APPLICATIONS,21(4),715-724.
MLA Zhong, Guoqiang,et al."Joint learning of error-correcting output codes and dichotomizers from data".NEURAL COMPUTING & APPLICATIONS 21.4(2012):715-724.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhong, Guoqiang]'s Articles
[Huang, Kaizhu]'s Articles
[Liu, Cheng-Lin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhong, Guoqiang]'s Articles
[Huang, Kaizhu]'s Articles
[Liu, Cheng-Lin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhong, Guoqiang]'s Articles
[Huang, Kaizhu]'s Articles
[Liu, Cheng-Lin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.