Results 11 to 20 of about 368,237 (276)
Machine Learning Students Overfit to Overfitting
CoRR, 2022 5 pages, with appendix, TeachML workshop @ ECML ...
Matias Valdenegro-Toro +1 more
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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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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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Application of Decision Tree Algorithm for Edible Mushroom Classification
Journal of Applied Informatics and Computing, 2022 The purpose of this research is to classify the mushroom based on its characteristic to be in an edible class or poisonous one using the Decision Tree Algorithm.
Afika Rianti +3 more
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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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ENphylo: A new method to model the distribution of extremely rare species
Methods in Ecology and Evolution, 2023 Species distribution models (SDMs) are a useful mean to understand how environmental variation influences species geographical distribution. SDMs are implemented by several different algorithms.
Alessandro Mondanaro +7 more
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Local Augment: Utilizing Local Bias Property of Convolutional Neural Networks for Data Augmentation
IEEE Access, 2021 Data augmentation is an effective way to increase the diversity of existing training datasets that result in improved generalization ability of convolutional neural networks (CNNs). The augmentation effect is usually global for the existing methods i.e.,
Youmin Kim +2 more
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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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Entropy, 2021 Model selection criteria are widely used to identify the model that best represents the data among a set of potential candidates. Amidst the different model selection criteria, the Bayesian information criterion (BIC) and the Akaike information criterion
Luca Spolladore +3 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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