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A method based on support vector machines is proposed for cancer molecular classification. The Bhattacharyya distance of each gene is calculated as the criterion for filtering 'noisy-genes' which do not contribute to classification, and then based on the expression values of the informative genes, a linear support vector machine is used as the classifier to classify different cancers. This process has been applied to the human acute leukemia dataset as a test case. The effectiveness and feasibility of the method is proved by the results that all the samples in the dataset can be correctly classified without any error.