Results 81 to 90 of about 1,862,731 (241)

A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition

International Journal of Applied Mathematics and Computer Science, 2023
Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency.
Karlupia Namrata, Mahajan Palak, Abrol Pawanesh, Lehana Parveen K.   +3 more
doaj   +1 more source

Score model network: Optimization hyperparameters.

Score model network: Optimization hyperparameters.
Christopher Frederik Blum (17972603), Stefan Harmeling (17972609), Julius Ramakers (17972600), Markus Kollmann (25296), Sabrina König (17972606)   +4 more
core   +1 more source

Robust optimization of SVM hyperparameters in the classification of bioactive compounds [PDF]

, 2015
Background: Support Vector Machine has become one of the most popular machine learning tools used in virtual screening campaigns aimed at finding new drug candidates. Although it can be extremely effective in finding new potentially active compounds, its
Podlewska, Sabina, Czarnecki, Wojciech, Wojciech M Czarnecki, Bojarski, Andrzej J., Sabina Podlewska, Andrzej J Bojarski   +5 more
core   +1 more source

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

Hyperparameter Optimization for Effort Estimation

CoRR, 2018
Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g.
Tianpei Xia, Rahul Krishna, Jianfeng Chen, George Mathew, Xipeng Shen, Tim Menzies   +5 more
openaire   +3 more sources

Bayesian Optimization of Hyperparameters in Machine Learning [PDF]

, 2021
Cilj naše diplomske naloge je bil analizirati Bayesovsko optimizacijo na problemu optimizacije hiperparametrov. Podlaga za analizo sta pogosto uporabljani orodji za optimizacijo hiperparametrov: naključno iskanje in iskanje v mreži.
OCEPEK, DAVID
core  

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

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

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

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