Multiconlitron is a general theoretical framework for constructing piecewise linear classifier. However, it contains a relatively large number of linear functions, resulting in complicated model structure and poor generalization ability. Learning to prune redundant or excessive components may be a very necessary progression. We propose a novel greedy method, i.e., greedy support multiconlitron algorithm (GreSMA) to simplify the multiconlitron. In GreSMA, a procedure of greedy selection is first used. It generates …