Accelerate the Convergence Speed of Perceptron Learning Algorithm with Weight

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