Support vector machine (SVM) is a new statistical learning method.Compared with the classical machine learning methods

the learning discipline of SVM is to minimize the structural risk instead of empirical risk used in the learning discipline of classical methods

and SVM gives better generative performance.Because SVM algorithm is a convex quadratic optimization problem

the local optimal solution is certainly the global optimal one.In this paper

SVM algorithm is applied to detect the micro-calcifications in mammogram for the first time.The algorithm is tested with mammograms of clinical patients and results show that SVM method achieves a higher true positive in comparison with artificial neural network (ANN) based on the empirical risk minimization

and is valuable for application in clinical engineering.