The Bounds on the Rate of Uniform Convergence of Learning Process with Rough Samples

Author

Shicheng, Hu ; Yongdong, Xu ; Yang, Liu

Author_Institution

Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol. at Weihai, Weihai, China

Volume

3

fYear

2010

fDate

11-12 May 2010

Firstpage

722

Lastpage

725

Abstract

Support vector machine is a research hotspot in the area of machine learning, and the bounds on the rate of uniform convergence of statistical learning theory describe the extended ability of learning machine based on ERM. In the paper, Rough Empirical Risk Minimization (RERM) principle is proposed, and the bounds on the rate of uniform convergence of learning process with rough samples are presented and proven, they provide a theoretical basis for the research of rough support vector machine. Which has a wide range of applications in Natural Language Processing, including automatic summarization, text classification, etc.

Keywords

learning (artificial intelligence); rough set theory; statistical analysis; support vector machines; RERM; automatic summarization; learning process; machine learning; natural language processing; rough empirical risk minimization; rough samples; statistical learning theory; support vector machine; text classification; uniform convergence; Automation; Computer science; Convergence; Learning systems; Machine learning; Risk management; Statistical learning; Statistics; Support vector machine classification; Support vector machines; Rough samples; SVM; the bounds;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-7279-6

Electronic_ISBN

978-1-4244-7280-2

Type

conf

DOI

10.1109/ICICTA.2010.775

Filename

5523044