Several kinds of current boundary classification methods based on hyperplane, hypersphere or ellipsoid were studied, and a novel classification method called Maximum-margin Fuzzy Classifier (MFC) was proposed by using a space point as the classification criterion. By the proposed method, a fuzzy classified point c was chosen in the pattern space firstly, which should be as close to two classes as possible. Moreover, the angle between the two classes should be also as large as possible. Then, the testing points could be classified in terms of the maximum angular margin between c and all the training points. Finally, the applications of the MFC were popularized from two-class classification to one-class classification according to the kernel dual of MFC equivalent to the Minimum Enclosed Ball (MEB). Comparative experiments with current classification methods verify that the MFC has good classification performance and noise resistance ability and its classification accuracy has been reached 98.9%.