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Strengthening the Forward Variable Selection Stopping Criterion
2009Given any modeling problem, variable selection is a preprocess step that selects the most relevant variables with respect to the output variable. Forward selection is the most straightforward strategy for variable selection; its application using the mutual information is simple, intuitive and effective, and is commonly used in the machine learning ...
Luis Javier Herrera +5 more
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Stopping Criterion for Active Learning with Model Stability
ACM Transactions on Intelligent Systems and Technology, 2017Active learning selectively labels the most informative instances, aiming to reduce the cost of data annotation. While much effort has been devoted to active sampling functions, relatively limited attention has been paid to when the learning process should stop.
Yexun Zhang +3 more
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Early stopping criterion for LDPC
2017 International Conference on Circuits, System and Simulation (ICCSS), 2017A log-likelihood ratio (LLR) test on the single Mth check node of an LDPC decoder is utilised to develop an improved low complexity stopping criterion. The average number of iterations required at low SNRs is significantly reduced without sacrificing the BER performance.
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A fast automatic stopping criterion for anisotropic diffusion
IEEE International Conference on Acoustics Speech and Signal Processing, 2002In previous work we developed, apparently for the first time, an automatic criterion to choose when to stop the iteration in anisotropic diffusion signal reconstruction. However the method was computationally very intensive. Here, using asymptotic perturbation methods we derive an approximation to the criterion that is quite accurate but can be ...
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Stability-Based Stopping Criterion for Active Learning
2014 IEEE International Conference on Data Mining, 2014While active learning has drawn broad attention in recent years, there are relatively few studies on stopping criterion for active learning. We here propose a novel model stability based stopping criterion, which considers the potential of each unlabeled examples to change the model once added to the training set.
Wenquan Wang, Wenbin Cai, Ya Zhang 0002
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A Stopping Criterion for a Dynamic Competing Risks Model
Methodology And Computing In Applied Probability, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Incremental learning with a stopping criterion experimental results
1995We recently proposed a new incremental procedure for supervised learning with noisy data. Each step consists in adding to the current network a new unit (or small 2- or 3-neuron networks) which is trained to learn the error of the network. The incremental step is repeated until the error of the current network can be considered as a noise. The stopping
Rachida Chentouf, Christian Jutten
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Variance as a Stopping Criterion for Genetic Algorithms with Elitist Model
Fundamenta Informaticae, 2012Genetic Algorithm (GA) has now become one of the leading mechanisms in providing solution to complex optimization problems. Although widely used, there are very few theoretical guidelines for determining when to stop the algorithm. This article establishes theoretically that the variance of the best fitness values obtained in the iterations can be ...
Dinabandhu Bhandari +2 more
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A cost-benefit stopping criterion for statistical testing
37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the, 2004Determining when to stop a statistical test is an important management decision. Several stopping criteria have been proposed, including criteria based on statistical similarity, the probability that the system has a desired reliability, and the expected cost of remaining faults.
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Stopping criterion for anisotropic image diffusion
Optik, 2016Abstract Coherence-enhancing diffusion filtering is a striking application of the anisotropic diffusion in image processing. The technique deals with the problem of completion of interrupted lines and enhancement of flow-like features in fingerprint images.
Mohammad A.U. Khan +3 more
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