Support Vector Machines (SVMs) have shown
a great potential in numerous visual learning and pattern
recognition problems.
The optimal decision surface of a SVM is constructed
from its support vectors which are conventionally determined by solving
a quadratic programming (QP) problem.
However, solving a large optimization problem is challenging
since it is computationally intensive and the memory requirement
grows with square of the training vectors.
In this paper, we propose a geometric method to extract a small
superset of support vectors, which we call guard vectors,
to construct the optimal decision surface.
Specifically, the guard vectors are found by solving
a set of linear programming problems.
Experimental results on synthetic and real data sets
show that the proposed method is more efficient than conventional
methods using QPs and requires much less memory
(To appear in CVPR 2000).