Ezperimental Comparison of Semi-Supervised Learning Method Based on Kernels Strategy
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.
Semi-Supervised Learning Kernel Selection Measure Classification
Kai Li Xinyong Chen
School of Mathematics and Computer, Hebei University, Baoding 071002 Key Lab. In Machine Learning and Computational Intelligence of Hebei Province, Baoding 071002 China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
4002-4006
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）