Partial Learning Machine: A New Learning Scheme for Feed Forward Neural Networks

Abstract

As for the traditional Feed Forward Neural Network (FNN) learning methods such as Back-Propagation (BP) algorithms, the training methods waste much time with poor generalization ability. And for another learning scheme of FNN, Extreme Learning Machine (ELM), the training speed is increased dramatically with the cost of low accuracy and large fluctuation. In this paper, a new machine learning scheme named Partial Learning Machine (PLM) is proposed based on heuristic adjustment to general problems. The experimental results show that the training time of PLM is less than that of BP with better generalization ability. Moreover, PLM has less fluctuation and higher learning precision than ELM.