神经网络中基于在线指标相关性的早停法
Early stopping by correlating online indicators in neural networks
AI总结：
本文提出一种利用在线指标相关性识别过拟合的早停方法，通过金丝雀函数（交叉验证）构建停止条件，在解析器生成任务上验证其优于单一标准方法，提升泛化性能。
摘要：
为了最小化神经网络中的泛化误差，本文正式引入了一种在训练学习器时识别过拟合现象的新技术。这支持了可靠且可信的早停条件，从而提高了该类建模的预测能力。我们的方案利用了在线指标集合随时间变化的相关性，这些指标是用于指示一组假设是否满足的特征函数，与基于金丝雀判断构建的一系列独立停止条件相关联，以评估过拟合的存在。这样，我们为中断学习过程的决策提供了正式依据。与以往专注于单一标准的方法不同，我们利用了独立评估之间的互补性，从而寻求更宽的工作范围和更高的诊断可靠性。为了说明所描述停止条件的有效性，我们选择在自然语言处理领域开展工作，这是一个日益基于机器学习的操作连续体。作为案例研究，我们专注于解析器生成，这是该领域中最具挑战性和最复杂的任务之一。选择交叉验证作为金丝雀函数，使得我们能够与基于过拟合识别的最具代表性的早停条件进行实际比较，指向了朝向最优偏差和方差控制的有前景的开端。
英文摘要：
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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