Accelerate the Convergence Speed of Perceptron Learning Algorithm with Weight

Liuyang Zhao, Shifeng Li, Ye Tian, Yue Wang

Abstract: The main problems of the traditional perceptron learning algorithm (PLA) is that there are too many iterations and it is difficult to generate a model quickly, and more iterations are needed when the boundary between the two classes is closed. In this paper, we improve PLA by introducing the current weight into the updating formulation, which can significantly accelerate the iteration. The experiments on different public datasets show that our proposed method can greatly improve the speed of the traditional PLA.