Results 11 to 20 of about 37,831 (263)
Algorithms that Satisfy a Stopping Criterion, Probably [PDF]
Vietnam Journal of Mathematics, 2015 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.
Ascher, Uri, Roosta-Khorasani, Farbod
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Intersection of Parallels as an Early Stopping Criterion
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022 CIKM ...
Ali Vardasbi +2 more
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Sequential Design-Space Reduction and Its Application to Hull-Form Optimization
Journal of Marine Science and Engineering, 2023 Hull-form optimization is a complex engineering problem. Owing to the several numerical simulations and complex design-performance spaces, hull-form optimization is considered an inefficient process, which makes determining the global optimum difficult ...
Zu-Yuan Liu +4 more
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IET Generation, Transmission & Distribution, 2023 The carbon capture and storage (CCS) technique gains much attention due to its role in reducing CO2 emissions. By introducing flexible CCS devices into conventional power plants, the low‐carbon economic operation of the power system can be achieved ...
Xiaosheng Zhang +6 more
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A Stopping Criterion for Transductive Active Learning
, 2023 AbstractIn transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle to provide the right label at some cost or use the prediction of a classifier which we train on the labels acquired so far. In contrast, the commonly used (inductive) active learning aims to select
Daniel Kottke +3 more
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A stopping criterion for active learning [PDF]
Computer Speech & Language, 2008 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
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Neighborhood-Based Stopping Criterion for Contrastive Divergence [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2017 Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence (CD) learning algorithm, an approximation to the gradient of the data log-likelihood (logL).
Romero Merino, Enrique +2 more
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Early stop criterion from the bootstrap ensemble [PDF]
1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002 This paper addresses the problem of generalization error estimation in neural networks. A new early stop criterion based on a Bootstrap estimate of the generalization error is suggested. The estimate does not require the network to be trained to the minimum of the cost function, as required by other methods based on asymptotic theory.
Lars Kai Hansen, Jan Larsen, Torben Fog
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Applied Mathematics in Science and Engineering, 2022 In this paper, we consider the simultaneous inversion of the source term and the initial data for a time-fractional diffusion equation based on the additional temperatures at two fixed times $ t=T_{1} $ and $ t=T_{2} $ . The inverse problem is formulated
Jin Wen, Zhuan-Xia Liu, Shan-Shan Wang
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Advances in Electrical and Computer Engineering, 2014 This paper proposes an iteration stopping criterion for turbo decoding with Benedetto's decoding algorithm based on a posteriori probabilities. This stopping criterion is used in two schemes.
SAVIN, A., TRIFINA, L., ANDREI, M.
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