Many kernelized classification methods

such as SVM and SVDD

are formulated as quadratic programming (QP) problems

but computing kernel matrix would require

O

(

m

2

) computation

and solving QP may take up to

O

(

m

3

)

which limits these methods to train on large datasets.In this paper

a new classification method called Maximum Vector-Angular Margin Classifier (MAMC) is proposed

based on a new concept of margin called vector-angular margin

to find an optimal vector c in patterns' feature space and all the testing points can be classified in terms of the maximum vector-angular margin

ρ

between the vector c and all the training points.Meanwhile

the ker

nelized MAMC can be equivalently formulated as the kernelized Minimum Enclosing Ball (MEB)

and thus MAMC can be extended to Maximum Vector-Angular Margin Core Vector Machine (MAM-CVM) by introducing Core Vector Machine (CVM) method

to solve the training and classification for large datasets.Experimental results on artificial and real datasets are provided to validate the effectiveness of the proposed methods here.