SGD method for entropy error function with smoothing l0 regularization for neural networks

TL;DR

This paper introduces a new entropy function with smoothing l0 regularization for neural networks, improving convergence speed and classification accuracy over traditional methods, supported by empirical results on real datasets.

Contribution

It proposes a novel entropy function with smoothing l0 regularization, enhancing neural network training effectiveness and prediction accuracy.

Findings

Improved prediction performance on real-world datasets.

More precise classifications compared to baseline algorithms.

Enhanced learning effectiveness in neural networks.

Abstract

The entropy error function has been widely used in neural networks. Nevertheless, the network training based on this error function generally leads to a slow convergence rate, and can easily be trapped in a local minimum or even with the incorrect saturation problem in practice. In fact, there are many results based on entropy error function in neural network and its applications. However, the theory of such an algorithm and its convergence have not been fully studied so far. To tackle the issue, we propose a novel entropy function with smoothing l0 regularization for feed-forward neural networks. Using real-world datasets, we performed an empirical evaluation to demonstrate that the newly conceived algorithm allows us to substantially improve the prediction performance of the considered neural networks. More importantly, the experimental results also show that our proposed function brings in more precise classifications, compared to well-founded baselines. Our work is novel as it enables neural networks to learn effectively, producing more accurate predictions compared to state-of-the-art algorithms. In this respect, we expect that the algorithm will contribute to existing studies in the field, advancing research in Machine Learning and Deep Learning.