Results 1 to 10 of about 131,851 (273)

Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization [PDF]

Biology, 2022
Depending on excellent prediction ability, machine learning has been considered the most powerful implement to analyze high-throughput sequencing genome data.
Mang Liang, Bingxing An, Keanning Li, Lili Du, Tianyu Deng, Sheng Cao, Yueying Du, Lingyang Xu, Xue Gao, Lupei Zhang, Junya Li, Huijiang Gao   +11 more
doaj   +4 more sources

Parsimonious Optimization of Multitask Neural Network Hyperparameters [PDF]

Molecules, 2021
Neural networks are rapidly gaining popularity in chemical modeling and Quantitative Structure–Activity Relationship (QSAR) thanks to their ability to handle multitask problems.
Cecile Valsecchi, Viviana Consonni, Roberto Todeschini, Marco Emilio Orlandi, Fabio Gosetti, Davide Ballabio   +5 more
doaj   +3 more sources

Combining K-fold cross validation with bayesian hyperparameter optimization for accuracy enhancement of land cover and land use classification [PDF]

Scientific Reports
Land cover and land use (LCLU) information is crucial in different earth observation applications, such as environmental management, infrastructure planning, and urban development.
Pooya Heidari, Asghar Milan
doaj   +2 more sources

A Survey on Hyperparameters Optimization of Deep Learning for Time Series Classification

IEEE Access
Time series classification (TSC) is essential in various application domains to understand the system dynamics. The adoption of deep learning has advanced TSC, however its performance is sensitive to hyperparameters configuration.
Ayuningtyas Hari Fristiana, Syukron Abu Ishaq Alfarozi, Adhistya Erna Permanasari, Mahardhika Pratama, Sunu Wibirama   +4 more
doaj   +3 more sources

Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm.

PLoS ONE, 2021
Deep Reinforcement Learning (DRL) enables agents to make decisions based on a well-designed reward function that suites a particular environment without any prior knowledge related to a given environment.
Nesma M Ashraf, Reham R Mostafa, Rasha H Sakr, M Z Rashad   +3 more
doaj   +2 more sources

Enhancing Load Prediction Accuracy using Optimized Support Vector Regression Models

Journal of Digital Food, Energy & Water Systems, 2023
This paper investigates the effect of Support Vector Regression hyperparameters optimization on electrical load prediction. Accurate and robust load prediction helps policy makers in the energy sector to make inform decision and reduce losses.
Abdulsemiu Olawuyi, Titus Ajewole, Muyideen Lawal, Tolulope Awofolaju   +3 more
doaj   +1 more source

Machine learning based on landslide susceptibility assessment with Bayesian optimized the hyperparameters

地质科技通报, 2022
In machine learning-based landslide susceptibility assessment, there are some differences in the evaluation results obtained by using different hyperparameters.
Can Yang, Leilei Liu, Yili Zhang, Wenqing Zhu, Shaohe Zhang   +4 more
doaj   +1 more source

Learning Multiple Defaults for Machine Learning Algorithms [PDF]

, 2021
The performance of modern machine learning methods highly depends on their hyperparameter configurations. One simple way of selecting a configuration is to use default settings, often proposed along with the publication and implementation of a new ...
Bischl, Bernd, Müller, Andreas, Pfisterer, Florian, Probst, Philipp, van Rijn, Jan N.   +4 more
core   +3 more sources

Metamodel-Based Hyperparameter Optimization of Optimization Algorithms in Building Energy Optimization

Buildings, 2023
Building energy optimization (BEO) is a promising technique to achieve energy efficient designs. The efficacy of optimization algorithms is imperative for the BEO technique and is significantly dependent on the algorithm hyperparameters.
Binghui Si, Feng Liu, Yanxia Li
doaj   +1 more source

Hyperparameter Optimization of CNN for Map Building

Современные информационные технологии и IT-образование, 2020
This article describes an approach for solving the task of finding hyperparameters of an artificial neural network, which is used for making a 2D land map.
Alexandra Akinina, Mikhail Nikiforov
doaj   +1 more source