Invariances in Classification: an efficient SVM implementation
Often, in pattern recognition, complementary knowledge is available. This could be useful to improve the performance of the recognition system. Part of this knowledge regards invariances, in particular when treating images or voice data. Many approaches have been proposed to incorporate invariances in pattern recognition systems. Some of these approaches require a pre-processing phase, others integrate the invariances in the algorithms. We present a unifying formulation of the problem of incorporating invariances into a pattern recognition classifier and we extend the SimpleSVM algorithm [Vishwanathan et al., 2003] to handle invariances efficiently.