Auxiliary Problems in Convex Alternating Structure Optimization Algorithm

被引：0

作者：

Zhang, Taozheng ^{[1
]}

Wang, Xiaojie ^{[2
]}

Chai, Jianping ^{[1
]}

机构：

[1] Commun Univ China, Informat Engn Sch, Beijing, Peoples R China

[2] Beijing Univ Posts & Telecommun, Ctr Intelligence Sci & Technol, Beijing, Peoples R China

来源：

INFORMATION TECHNOLOGY APPLICATIONS IN INDUSTRY, PTS 1-4 | 2013年 / 263-266卷

基金：

中国国家自然科学基金;

关键词：

Artificial intelligence; Convex Alternating Structure Optimization(cASO); Auxiliary problems(APs); Target problems(TPs); Relevancy; Orthogonality; Domain adaptation;

D O I：

10.4028/www.scientific.net/AMM.263-266.2349

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

There exist principles of Relevancy, Orthogonality and Domain Adaptation for auxiliary problems (APs) selection in Alternating Structure Optimization (ASO) algorithm. Convex Alternating Structure Optimization (cASO) algorithm is an improved one based on ASO algorithm, whose kernel still lies in creating excellent APs. In order to validate the effectiveness of the preceding principles in cASO algorithm, many types of APs are created by taking example of Chinese syntactic chunking. Experimental results and analyses both demonstrate that those principles still hold in cASO algorithm.

引用

页码：2349 / +

页数：2

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