This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early...

We propose an effective stopping criterion for higher-order fast sweeping schemes for static Hamilton-Jacobi equations based on ratios of three consecutive iterations. To design the new stopping criterion we analyze the convergence of the first-order Lax-Friedrichs sweeping scheme by using the theory of nonlinear iteration. In addition, we propose a fifth-order Weighted PowerENO sweeping scheme...

In this paper, we address the issue of deciding when to stop active learning for building a labeled training corpus. Firstly, this paper presents a new stopping criterion, classification-change, which considers the potential ability of each unlabeled example on changing decision boundaries. Secondly, a multi-criteriabased combination strategy is proposed to solve the problem of predefining an a...

We develop, apparently for the first time, an automatic criterion to choose when to stop the iteration in anisotropic diffusion signal reconstruction.

The stopping problem with variance as the optimality criterion is introduced. Due to the variance criterion, smooth fit cannot be applied directly. The problem is solved by embedding it into tractable auxiliary optimal stopping problems, where smooth fit is used to obtain explicit, optimal solutions. Optimal strategies are presented in closed form for several examples. A characteristic feature ...

Iterative numerical algorithms are typically equipped with a stopping criterion, where the iteration process is terminated when some error or misfit measure is deemed to be below a given tolerance. This is a useful setting for comparing algorithm performance, among other purposes. However, in practical applications a precise value for such a tolerance is rarely known; rather, only some possibly...

Genetic Algorithms have been successfully applied in a wide variety of problems. Although widely used, there are few theoretical guidelines for determining when to terminate the search. One result by Aytug and Koehler provides a loose bound on the number of GA generations needed to see all populations (and hence, an optimal solution) with a specified probability. In this paper we derive a tight...

Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on applying AL to natural language processing tasks reporting impressive savings, little work has been done on defining a stopping criterion. In this work, we present a stopping criterion for active learning based on the...