Stochastic vector machine: a multi-layered network with learning ability

Summary form only given. The authors examine internal representations of a stochastic vector machine (SVM) and compare its learning ability with that of the backpropagation (BP) model through the experiments of handwritten Chinese character recognition. The experimental results show two facts. One is that local or distributed representation of input patterns is developed, depending on the structure of SVM. The other is that the recognition rate of the SVM is almost equal to that of the BP model, so its learning ability can be considered to be comparable to that of BP model.<>