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Accelerate the Convergence Speed of Perceptron Learning Algorithm with Weight
Liuyang Zhao, Shifeng Li, Ye Tian, Yue Wang
204 - 210
10.3233/FAIA220385
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 358: Fuzzy Systems and Data Mining VIII
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.

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