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arXiv · 2402.02513

Early stopping by correlating online indicators in neural networks

Manuel Vilares Ferro·Yerai Doval Mosquera·Francisco J. Ribadas Pena·Victor M. Darriba Bilbao
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Abstract

In order to minimize the generalization error in neural networks, a novel technique to identify overfitting phenomena when training the learner is formally introduced. This enables support of a reliable and trustworthy early stopping condition, thus improving the predictive power of that type of modeling. Our proposal exploits the correlation over time in a collection of online indicators, namely characteristic functions for indicating if a set of hypotheses are met, associated with a range of independent stopping conditions built from a canary judgment to evaluate the presence of overfitting. That way, we provide a formal basis for decision making in terms of interrupting the learning process. As opposed to previous approaches focused on a single criterion, we take advantage of subsidiarities between independent assessments, thus seeking both a wider operating range and greater diagnostic reliability. With a view to illustrating the effectiveness of the halting condition described, we choose to work in the sphere of natural language processing, an operational continuum increasingly based on machine learning. As a case study, we focus on parser generation, one of the most demanding and complex tasks in the domain. The selection of cross-validation as a canary function enables an actual comparison with the most representative early stopping conditions based on overfitting identification, pointing to a promising start toward an optimal bias and variance control.

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Manuel Vilares Ferro, Yerai Doval Mosquera, Francisco J. Ribadas Pena, Victor M. Darriba Bilbao. 2024-02-04. Early stopping by correlating online indicators in neural networks. https://doi.org/10.1016/j.jcss.2022.05.002

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