Through the entropy estimation, information theory-based learning is performed locally at each neuron. The
input data are classified by using the weighted statistical information from all the neurons. Classification method based on
multi-layer structure and information results in a better performance in data classification than many other existing methods
of neural networks. This architecture can extend to a large embedded system to handle complex financial problems.