Experimental comparison of semi-supervised learning method based on kernels strategy

Author

Li, Kai ; Chen, Xinyong

Author_Institution

Sch. of Math. & Comput., Hebei Univ., Baoding, China

fYear

2009

fDate

17-19 June 2009

Firstpage

4002

Lastpage

4006

Abstract

Using the generalized kernel consistency method, the semi-supervised learning algorithm named GCM (Generalized Consistency Method) which based on kernel strategy is presented in this paper. Five different measures and the interrelations among them are also deeply analyzed. Relation between arguments of different measures and performance of algorithm is experimentally studied, and performance of GCM algorithm with different measures is compared with each other. Experimental results show that performance of GCM algorithm with the exponential measure is superior to one with other measures and performance of GCM algorithm with the Euclidean measure is inferior to one with other measures. Moreover, some arguments of different measures have a certain effect on the performance of algorithm.

Keywords

learning (artificial intelligence); GCM; generalized consistency method; generalized kernel consistency method; semi-supervised learning; Artificial intelligence; Clustering algorithms; Kernel; Learning systems; Machine learning; Machine learning algorithms; Mathematics; Semisupervised learning; Supervised learning; Unsupervised learning; Classification; Kernel; Measure; Selection; Semi-Supervised Learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192637

Filename

5192637