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

Authors:
Kai Li;Xinyong Chen
Affiliations:
School of Mathematics and Computer, Hebei University, Baoding and Key Lab. In Machine Learning and Computational Intelligence of Hebei Province, Baoding, China;School of Mathematics and Computer, Hebei University, Baoding and Key Lab. In Machine Learning and Computational Intelligence of Hebei Province, Baoding, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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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.