Histogram clustering and hybrid classifier for handwritten Arabic characters recognition
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Hybrid Evolution of Heterogeneous Neural Networks
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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The problem of handwritten digit recognition is tackled by multi-layer feedforward neural networks with different types of neuronal activation functions. Three types of activation functions are adopted in the network, namely, the traditional sigmoid function, the sinusoidal function and a periodic function that can be considered as a combination of the first two functions. To speed up the learning, as well as to reduce the network size, the Extended Kalman Filter (EKF) algorithm conjunct with a pruning method is used to train the network. Simulation results show that periodic activation functions perform better than monotonic ones in solving multi-cluster classification problems such as handwritten digit recognition.