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.

翻译：为最小化神经网络的泛化误差，本文正式提出一种在训练学习器时识别过拟合现象的新技术。该方法可为可靠且可信的早停条件提供支持，从而提升此类建模的预测能力。本方案利用一组在线指标（即用于判断假设集合是否成立的特征函数）随时间变化的关联性，这些指标与基于金丝雀判据构建的多个独立早停条件相关联，用于评估过拟合是否出现。由此，我们为中断学习过程的决策提供了形式化基础。与先前聚焦单一准则的方法不同，我们充分利用各独立评估之间的互补性，旨在同时追求更宽的工作范围和更高的诊断可靠性。为说明所提中止条件的有效性，我们选择在自然语言处理领域（这一日益依赖机器学习的连续操作环境）进行验证。作为案例研究，我们聚焦于句法分析器生成——该领域最具挑战性和复杂性的任务之一。采用交叉验证作为金丝雀函数，使我们能够与最具代表性的基于过拟合识别的早停条件进行实际对比，为向最优偏差-方差控制迈进提供了有希望的开端。