Some geometry of Support Vector Machines for classification and regression is described and proven.And then the generalization performance of SVMs on newly-added samples is discussed.Through the analysis of the property of newly-added samples and the effect of them on support vectors and non-support vectors

some valuable results are presented.These enable us to conclude that SVM has a good compatibility

adaptability and generalization performance for newly-added samples and is a hereditable learning model.