Results 11 to 20 of about 173,823 (173)
Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability
Frontiers in Neurology, 2020 Objective: To evaluate various machine learning algorithms in predicting peripheral vestibular dysfunction using the dataset of the center of pressure (COP) sway during foam posturography measured from patients with dizziness.Study Design: Retrospective ...
Teru Kamogashira +5 more
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A hierarchical optimisation framework for pigmented lesion diagnosis
CAAI Transactions on Intelligence Technology, 2022 The study of training hyperparameters optimisation problems remains underexplored in skin lesion research. This is the first report of using hierarchical optimisation to improve computational effort in a four‐dimensional search space for the problem. The
Audrey Huong +3 more
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Applied Sciences, 2022 Hyperparameter tuning is a critical function necessary for the effective deployment of most machine learning (ML) algorithms. It is used to find the optimal hyperparameter settings of an ML algorithm in order to improve its overall output performance. To
Ismail Damilola Raji +5 more
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Sensors, 2021 Electrical resistivity is used to obtain various types of information for soil strata. Hence, the prediction of electrical resistivity is helpful to predict the future behavior of soil.
Seung-Jae Lee, Hyung-Koo Yoon
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Diagnostics, 2022 Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy.
Masyitah Abu +6 more
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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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Remote Sensing, 2022 Aflaj (plural of falaj) are tunnels or trenches built to deliver groundwater from its source to the point of consumption. Support vector machine (SVM) and extreme gradient boosting (XGB) machine learning models were used to predict groundwater aflaj ...
Khalifa M. Al-Kindi, Saeid Janizadeh
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AutoRL Hyperparameter Landscapes
International Conference on AutoML, 2023 Although Reinforcement Learning (RL) has shown to be capable of producing impressive results, its use is limited by the impact of its hyperparameters on performance. This often makes it difficult to achieve good results in practice. Automated RL (AutoRL) addresses this difficulty, yet little is known about the dynamics of the hyperparameter landscapes ...
Mohan, Aditya +4 more
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Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Accurately estimating and mapping forest structural parameters are essential for monitoring forest resources and understanding ecological processes. The novel deep learning algorithm has the potential to be a promising approach to improve the estimation ...
Hao Liu +7 more
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No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
, 2022 The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly on the environment can be financially prohibitive, dangerous, or time consuming.
Wang, Han +9 more
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