Results 11 to 20 of about 98,022 (269)
Collaborative hyperparameter tuning
, 2013 International audienceHyperparameter learning has traditionally been a manual task because of the limited number of trials. Today's computing infrastructures allow bigger evaluation budgets, thus opening the way for algorithmic approaches.
Bardenet, R. +3 more
core +3 more sources
Refining the ONCE Benchmark With Hyperparameter Tuning
IEEE AccessIn response to the growing demand for 3D object detection in applications such as autonomous driving, robotics, and augmented reality, this work focuses on the evaluation of semi-supervised learning approaches for point cloud data.
Maksim Golyadkin +3 more
doaj +3 more sources
Robust algorithm to learn rules for classification: A fault diagnosis case study [PDF]
FME Transactions, 2023 Machine learning algorithms are used for building classifier models. The rule-based decision tree classifiers are popular ones. However, the performance of the decision tree classifier varies with hyperparameter tuning.
Balaji Arun P., Sugumaran V.
doaj +1 more source
A hyperparameter‐tuning approach to automated inverse planning [PDF]
Medical Physics, 2022 AbstractBackgroundIn current practice, radiotherapy inverse planning often requires treatment planners to modify multiple parameters in the treatment planning system's objective function to produce clinically acceptable plans. Due to the manual steps in this process, plan quality can vary depending on the planning time available and the planner's ...
Maass, Kelsey +2 more
openaire +3 more sources
High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
Algorithms, 2022 Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. However, just how useful is said tuning?
Moshe Sipper
doaj +1 more source
Journal of Ocean Engineering and Science, 2022 An accurate prediction of ship fuel consumption is critical for speed, trim, and voyage optimisation etc. While previous studies have focused on predicting ship fuel consumption with respect to a variety of factors, research on the impact of ...
Tianrui Zhou +3 more
doaj +1 more source
Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate
Frontiers in Public Health, 2021 This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction
Jayakumar Kaliappan +5 more
doaj +1 more source
Hyperparameter Tuning Approaches
, 2023 AbstractThis chapter provides a broad overview over the different hyperparameter tunings. It details the process of HPT, and discusses popular HPT approaches and difficulties. It focuses on surrogate optimization, because this is the most powerful approach.
Thomas Bartz-Beielstein +1 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
openaire +2 more sources
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
doaj +1 more source