A Fast Learning Algorithm with Transductive Support Vector Machine

Author

Xie Jian ; Dong Hua ; Li Ming ; Liu GaoHang

Author_Institution

Dept. of Key Lab. of Nondestructive Test (Minist. of Educ.), Nanchang HangKong Univ., Nanchang, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

Transductive inference based on support vector machine is a new research region in statistical learning theory. An improved algorithm is proposed in this paper, which overcome the disadvantages of studying process complexity and slow in the progressive transductive support vector machine learning algorithm. The algorithm optimized the samples which near the support vector only, and large number of samples were reduced, so the speed of algorithm is improved. Experiments show that the speed of this algorithm is improved with little influence on the performance.

Keywords

inference mechanisms; learning (artificial intelligence); statistical analysis; support vector machines; fast learning algorithm; machine learning; process complexity; statistical learning theory; transductive inference; transductive support vector machine; Inference algorithms; Laboratories; Learning systems; Machine learning; Machine learning algorithms; Nondestructive testing; Pattern recognition; Statistical learning; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5365411

Filename

5365411