Pupil Learning Mechanism
Rua-Huan Tsaih Yu-Hang Chien Shih-Yi Chien
Studies on artificial neural networks rarely address both vanishing gradients
and overfitting issues. In this study, we follow the pupil learning procedure,
which has the features of interpreting, picking, understanding, cramming, and
organizing, to derive the pupil learning mechanism (PLM) by which to modify the
network structure and weights of 2-layer neural networks (2LNNs). The PLM
consists of modules for sequential learning, adaptive learning, perfect
learning, and less-overfitted learning. Based upon a copper price forecasting
dataset, we conduct an experiment to validate the PLM module design modules,
and an experiment to evaluate the performance of PLM. The empirical results
indeed approve the PLM module design and show the superiority of the proposed
PLM model over the linear regression model and the conventional
backpropagation-based 2LNN model.