Title :
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
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;
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
DOI :
10.1109/CISE.2009.5365411
