Joint learning of error-correcting output codes and dichotomizers from data

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 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
发表期刊	NEURAL COMPUTING & APPLICATIONS
	2012-06-01
卷号	21 期号:4 页码:715-724
文章类型	Article
摘要	The 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.
关键词	Error Correcting Output Codes (Ecoc) Dichotomizer Concave-convex Procedure (Cccp)
WOS标题词	Science & Technology ; Technology
关键词[WOS]	CLASSIFICATION ; DESIGN
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000304160200011
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/7994
专题	多模态人工智能系统全国重点实验室_模式分析与学习
作者单位	Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 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.