Large Margin - Minimum Classification Error Using Sum Of Shifted Sigmoids As The Loss Function

We have developed a novel loss function that embeds large-margin classification into Minimum Classification Error (MCE) training. Unlike previous efforts this approach employs a loss function that is bounded, does not require incremental adjustment of the margin or prior MCE training. It extends the Bayes risk formulation of MCE using Parzen Window estimation to incorporate large-margin classification and develops a loss function that is a sum of shifted sigmoids. Experimental results show improvement in recognition performance when evaluated on the TIDigits database.