Modified learning for discrete multi-valued neuron
Jinping Chen, Shin-Fu Wu, Shie-Jue Lee · 2013
Discrete Multi-valued Neuron (MVN) was proposed for solving classification problems. The neuron has an activation function which is used to create an output value for an input instance. The learning algorithm associated with discrete MVN was designed for multi-class classification. However, the algorithm can never converge for the cases of two-class classification. In this paper, we propose a revised activation function to overcome this difficulty. A concept of tolerating areas is included. Another scheme adopting new targets is also proposed to work with discrete MVN. Simulation results show that the proposed ideas can improve the performance of discrete MVN.