Results 11 to 20 of about 167,467 (260)
The overfitted brain hypothesis [PDF]
Patterns, 2021 What is the purpose of dreaming? Many scientists have postulated a role for dreaming in learning, often with the aim of improving generative models. In this issue of Patterns, Erik Hoel proposes a novel hypothesis, namely, that dreaming provides a means to reduce overfitting.
Luke Y. Prince, Blake A. Richards
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Metabolites, 2020 In this paper, we discuss the validity of using score plots of component models such as partial least squares regression, especially when these models are used for building classification models, and models derived from partial least squares regression ...
Marta Bevilacqua, Rasmus Bro
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Methods for Developing a Process Design Space Using Retrospective Data
Pharmaceutics, 2023 Prospectively planned designs of experiments (DoEs) offer a valuable approach to preventing collinearity issues that can result in statistical confusion, leading to misinterpretation and reducing the predictability of statistical models.
Miquel Romero-Obon +6 more
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Quantifying Overfitting: Introducing the Overfitting Index
CoRR, 2023 In the rapidly evolving domain of machine learning, ensuring model generalizability remains a quintessential challenge. Overfitting, where a model exhibits superior performance on training data but falters on unseen data, is a recurrent concern. This paper introduces the Overfitting Index (OI), a novel metric devised to quantitatively assess a model's ...
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Hands-on training about overfitting.
PLoS Computational Biology, 2021 Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this ...
Janez Demšar, Blaž Zupan
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An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction
Machines, 2018 Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all
Hung-Cuong Trinh, Yung-Keun Kwon
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To Overfit, or Not to Overfit: Improving the Performance of Deep Learning-Based SCA [PDF]
, 2022 AFRICACRYPT ...
Rezaeezade, A. (author) +2 more
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CoRR, 2023 High complexity models are notorious in machine learning for overfitting, a phenomenon in which models well represent data but fail to generalize an underlying data generating process. A typical procedure for circumventing overfitting computes empirical risk on a holdout set and halts once (or flags that/when) it begins to increase. Such practice often
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IEEE Access, 2019 Different automated decision support systems based on artificial neural network (ANN) have been widely proposed for the detection of heart disease in previous studies. However, most of these techniques focus on the preprocessing of features only. In this
Liaqat Ali +5 more
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Macroeconomic Predictions Using Payments Data and Machine Learning
Forecasting, 2023 This paper assesses the usefulness of comprehensive payments data for macroeconomic predictions in Canada. Specifically, we evaluate which type of payments data are useful, when they are useful, why they are useful, and whether machine learning (ML ...
James T. E. Chapman, Ajit Desai
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