Improved SMO algorithm with different error costs
基于不同惩罚系数的SMO改进算法

When Keerthi's Sequential Minimal Optimization(SMO) algorithm is applied to the classification of unbalanced datasets,it not only leads to a poor classification performance but makes the result unstable.In order to overcome the difficulty,an improved SMO algorithm that used different error costs for different class was presented.Besides,the formula and the steps of the improved SMO algorithm were given.Experimental results show that our algorithm's ability of dealing with unbalanced datasets can be improved...