When training sets with uneven class sizes are used

the classification result based on support vector machine (SVM) is undesirably biased towards the class with more samples in the training set. That is to say

the larger the sample size

the smaller the classification error

whereas the smaller the sample size

the larger the classification error. This paper proposes weighted support vector machine algorithms based on the analysis of the cause of such problem

and this algorithm overcomes the drawback which standard support vector machine algorithm can't deal with each sample flexibly and compensates for the unfavorable impact caused by this bias. Such weighted support vector machines improve classification accuracy for class with small size at the cost of accuracy reduction for large size class

and can be applied to the case of regarding small sort of classification accuracy

such as fault diagnosis. The result of outdoor image recognition shows the effectiveness of this algorithm.