Abstract:
Support Vector Machines (SVMs) have been the
promising method in the field of machine learning. But for the
real applications there are still some drawbacks in SVMs. e.g. the
high training cost and too many support vectors. This paper
presents a novel method based on set covering to overcome these
drawbacks. called SC-SVMs. Some experiments on real data
show the effectiveness of this new method.
