Abstract: Traditionary support vector machines (SVMs) usually focus on edge patterns of data distribution, and support vectors (SVs) usually generates from these patterns. This paper proposes an alternative algorithm, which generates SVs from all training patterns. The sparsity of the algorithm is validated on most data sets far better than typical SVMs. The complexity of the algorithm in multiclass problems is merely equivalent to two class SVMs, which greatly solves the problems of too many variables or too many binary classifiers in multi-class SVMs.

Key words: support vector machines, sparsity, multi-class problems, generalization