A Learning Method by Stochastic Connection Weight Update

In this paper, we propose a learning method that updates a synaptic weight in probability which is proportional to an output error. Proposed method can reduce computational complexity of learning and at the same time, it can improve the classification ability. We point out that an example produces small output error does not contribute to update of a synaptic weight. As learning progresses, the number of the small error examples will be increasing compared to the big one is decreasing. This unbalance will cause of difficulty of learning large error examples. Proposed method cancels this phenomenon and improve the learning ability. Validity of proposed method is confirmed through computer simulation.