An Ensemble of Neural Networks Based Incremental Learning Algorithm with Weightsupdated Voting

In this paper,an ensemble of neural networks based incremental learning algorithm with weightsupdated voting is described.The algorithm defines the kernel function of the component neural network training data sets.The voting weights are updated based on the distance between the test pattern and the kernel function.This method can adaptively update the voting weights with the classification performance of the component neural network to the test pattern and it is more rational than the stable voting weights method in the ensemble of neural networks based incremental learning algorithm.Experimental results show that the algorithm with weightsupdated voting is more promising than that with stable weights voting method.