Results 31 to 40 of about 5,001,817 (246)
IEEE Access, 2021 As the main driving equipment of modern industrial production activities, if a motor fails, it causes serious consequences. Bearings are the component with the highest motor failure frequency.
Zhen Li, Yang Wang, Jianeng Ma
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Hyperparameter Optimization [PDF]
, 2019 Recent interest in complex and computationally expensive machine learning models with many hyperparameters, such as automated machine learning (AutoML) frameworks and deep neural networks, has resulted in a resurgence of research on hyperparameter optimization (HPO). In this chapter, we give an overview of the most prominent approaches for HPO.
Feurer, Matthias, Hutter, Frank
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Impact of Hyperparameter Optimization on Cross-Version Defect Prediction: An Empirical Study [PDF]
Jisuanji kexue yu tansuo, 2023 In the field of machine learning, hyperparameters are one of the key factors that affect prediction performance. Previous studies have shown that optimizing hyperparameters can improve the performance of inner-version defect prediction and cross-project ...
HAN Hui, YU Qiao, ZHU Yi
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Hyperparameter Optimization: A Spectral Approach
CoRR, 2017 We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters.
Elad Hazan +2 more
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A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 The performance of machine learning (ML) methods for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values.
Prativa Pokhrel, Alina Lazar
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Frugal Optimization for Cost-related Hyperparameters
AAAI Conference on Artificial Intelligence, 2021 The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost.
Qingyun Wu, Chi Wang, Silu Huang
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Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization [PDF]
E3S Web of Conferences, 2023 There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations.
Muliawan Nicholas Hans +2 more
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Optimizing Deep Learning Hyperparameters Using Interpolation-Based Optimization [PDF]
Control and Optimization in Applied MathematicsHyperparameter optimization (HPO) is essential for maximizing the performance of deep learning models. Traditional approaches, such as grid search and Bayesian Optimization (BO), are widely used but can be computationally expensive.
Michael Oluwaseun Ayansiji +1 more
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Journal of Marine Science and Engineering, 2021 Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based ...
Zhihui Hu +5 more
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Automatic Termination for Hyperparameter Optimization
, 2021 Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget,
Makarova, Anastasia +7 more
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