DRC-ACM: Accurate Analytical Center Machine for Classification

Analytical center machine （ACM） has remarkable generalization performance, which is based on analytical center of version space From the analysis of geometry of machine learning and principle of ACM, it is shown that some constraints are redundant to the description of version space Redundant constraints push analytical center away from that of the prime version space so that the generalization performance degrades, and at the same time redundant constraints slow down the classifier and reduce the efficiency of storage due to non sparsity of ACM To overcome the above problems, an incremental algorithm is proposed to delete redundant constraints and embed into the frame of ACM that yields a non redundancy, accurate analytical center machine for classification called DRC ACM Experiments with Heart, Thyroid, Banana datasets demonstrate the validity of DRC ACM