Results 71 to 80 of about 47,583 (209)

MTMEGPS: An R package for multi-trait and multi-environment genomic and phenomic selection using deep learning

Frontiers in Plant Science
Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often ...
Freddy Mora-Poblete, Javiera Valenzuela-Herrera, Matías Balach, Claudio J. Martínez-Araya, Carlos Maldonado   +4 more
doaj   +1 more source

Artificial Neural Network Hyperparameters Optimization: A Survey

, 2022
Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics.
Kadhim, Zahraa Saddi, Abdullah, Hasanen S., Ghathwan, Khalil Ibrahim   +2 more
core   +1 more source

On the optimization of hyperparameters in Gaussian process regression with the help of low-order high-dimensional model representation

, 2022
When the data are sparse, optimization of hyperparameters of the kernel in Gaussian process regression by the commonly used maximum likelihood estimation (MLE) criterion often leads to overfitting.
Ihara, Manabu, Manzhos, Sergei
core   +2 more sources

Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm

Frontiers in Oncology
Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span.
Muhammad Sami Ullah, Muhammad Attique Khan, Anum Masood, Olfa Mzoughi, Oumaima Saidani, Nazik Alturki   +5 more
doaj   +1 more source

A Statistical Approach to Provide Explainable Convolutional Neural Network Parameter Optimization

International Journal of Computational Intelligence Systems, 2019
Algorithms based on convolutional neural networks (CNNs) have been great attention in image processing due to their ability to find patterns and recognize objects in a wide range of scientific and industrial applications.
Saman Akbarzadeh, Selam Ahderom, Kamal Alameh   +2 more
doaj   +1 more source

No-Regret Bayesian Optimization with Unknown Hyperparameters

J. Mach. Learn. Res., 2019
ISSN:1532 ...
Felix Berkenkamp, Angela P. Schoellig, Andreas Krause 0001   +2 more
openaire   +5 more sources

Adaptive Optimizer for Automated Hyperparameter Optimization Problem

CoRR, 2022
The choices of hyperparameters have critical effects on the performance of machine learning models. In this paper, we present a general framework that is able to construct an adaptive optimizer, which automatically adjust the appropriate algorithm and parameters in the process of optimization.
openaire   +2 more sources

Goal-oriented sensitivity analysis of hyperparameters in deep learning

, 2023
International audienceTackling new machine learning problems with neural networks always means optimizing numerous hyperparameters that define their structure and strongly impact their performances.
Congedo, Pietro Marco, Poëtte, Gaël, Novello, Paul, Lugato, David   +3 more
core   +1 more source

Effect of hyperparameter tuning of machine learning algorithms on the modeling quality of the distribution of three mosquito species in Morocco

Journal of Intelligent Systems
The widespread use of machine learning algorithms in dataset modeling requires a thorough understanding of the various tools likely to improve the modeling quality.
Douider Meriem, Amrani Ibrahim, Abik Mounia   +2 more
doaj   +1 more source

Amortized Bayesian inference of Gaussian process hyperparameters [PDF]

, 2022
The application of Gaussian processes (GPs) is limited by the rather slow process of optimizing the hyperparameters of a GP kernel which causes problems especially in applications -- such as Bayesian optimization -- that involve repeated optimization of ...
Rehn, Aki
core