An application of SVM: alphanumeric character recognition

Summary form only given. The application of a stochastic vector machine (SVM) to alphanumeric character recognition is considered. The SVM is a new multilayered network with learning ability as in the backpropagation (BP) model. The system dynamics in the network is represented on the direct product space of the stochastic vector, so the network consists of units and states. The learning rule follows gradient decent formulation so as to minimize Kullback divergence between the output and the desired states. A preliminary recognition experiment on alphabetic characters was conducted, and SVM's internal representations were examined from weight patterns in the network. The experiment indicates that distributed or local representation is developed by the learning algorithm. A network system was constructed and applied to alphanumeric character recognition. Experimental results indicate that the SVM can perform as well as the BP model.<>