Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-02 Cooperative journals: 《计算机应用研究》
Abstract: To design a training model with stable computational performance and high accuracy which is applied on a small training dataset has been a difficult and challenging problem in the field of machine learning. This paper proposed a Resource Allocating Networks with Extended Kalman Filter (RANEKFs) based parallel ensemble learning algorithm. The learning system is composed of multiple RANEKF units, and the unit inputs are produced by the original dataset with random initialized weights. Based on the experiment results conducted on a small dataset, it is found that the novel model outperforms the ensemble learning systems constructed by the other artificial neural networks in terms of the computational accuracy and stability.