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Early stopping

In machine learning, early stopping is a form of regularization used to avoid overfitting when training a learner with an iterative method, such as… 
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2015
2015
Debugging Simulink models presents a significant challenge in the embedded industry. In this work, we propose SimFL, a fault… 
Review
2012
Review
2012
Interest in small–scale wind turbines as energy sources in the built environment has increased due to the desire of consumers in… 
2012
2012
In this paper, we develop new optional stopping theorems for scenarios where the stopping rules are defined by bounded continuity… 
2010
2010
Rapidly changing technology is one of the key triggers of system evolution. Some examples are: physically relocating a data… 
2010
2010
We describe an approach that can extend the utility of frictional cooling, originally developed for muon beams, to other… 
2009
2009
The most successful one-class classification methods are discriminative approaches aimed at separating the class of interest from… 
2006
2006
Turbo decoder estimates message values sent from transmitter by computing iteratively to get the maximum posteriori probability… 
2005
2005
This paper reports the statistical characteristics of phase fluctuations obtained by a very long baseline interferometry, which… 
1999
1999
This work presents a convergence theory for Dennis, El-Alem, and Maciel's class of trust-region-based algorithms for solving the… 
Review
1996
Review
1996
Working Papers are interim reports on work of the International Institute for Applied Systems Analysis and have received only…