Inspired by space geometry and beam angle, a maximum margin learning machine based on beam angle(BAMLM)
is proposed in this paper. The basic idea of BAMLM is to find a classified point in pattern space to separate two classes.
Meanwhile, the kernelized BAMLM is equivalent to the kernelized center-constrained minimum enclosing ball(CCMEB),
and BAMLM can be extended to BACVM by introducing core vector machine(CVM) which can solve the classification
for large-scale datasets. Experimental results obtained from synthetic and standard datasets show the effectiveness of the
proposed algorithms.