Results 91 to 100 of about 1,862,731 (241)

Hyperparameters tuning of random forest with harmony search in credit scoring [PDF]

, 2019
Correct identification of defaulters and non-defaulters in the lending industry is a crucial task for financial institutions. Credit scoring is a tool utilized for credit granting decisions.
Goh, Rui Ying, Adam, Mohd. Bakri, Lee, Lai Soon   +2 more
core   +1 more source

A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters

Mathematics
This paper introduces a novel hyperparameter optimization framework for regression tasks called the Combined-Sampling Algorithm to Search the Optimized Hyperparameters (CASOH).
Nguyen Huu Tiep, Hae-Yong Jeong, Kyung-Doo Kim, Nguyen Xuan Mung, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, Nguyen Ngoc Anh, Mai The Vu   +8 more
doaj   +1 more source

Selected backbone-specific optimization and loss hyperparameters.

Selected backbone-specific optimization and loss hyperparameters.
Shafiq Ul Rehman (23115148), Bassam W. Aboshosha (24376183), Lamees N. Mahmoud (17901488), Ibrahim Sadek (12515794)   +3 more
core   +1 more source

Hyperparameters of machine learning model obtained by Bayesian optimization.

Hyperparameters of machine learning model obtained by Bayesian optimization.
Xue Liu (420033), Chen Zhu (445894), Dong Chen (448284)   +2 more
core   +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

Hyperparameter Optimization in Machine Learning

Foundations and Trends® in Machine Learning
Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems based on these technologies.
Franceschi, Luca, Donini, Michele, Perrone, Valerio, Klein, Aaron, Archambeau, Cédric, Seeger, Matthias, Pontil, Massimiliano, Frasconi, Paolo   +7 more
openaire   +4 more sources

Hyperparameter Optimization

The Journal of The Institute of Image Information and Television Engineers, 2023
Marc Becker, Lennart Schneider, Sebastian Fischer   +2 more
openaire   +2 more sources

Feature-Based Population Initialization for Evolutionary Optimization of Machine Learning Models in Short-Term Solar Power Forecasting

Computation
Nowadays, solar energy is becoming one of the most popular sources of renewable energy worldwide. Traditional fossil fuels cause pollution and climate change, while solar power offers a clean and sustainable alternative.
Aleksei Vakhnin, Harri Niska, Anders V. Lindfors, Mikko Kolehmainen   +3 more
doaj   +1 more source

Bayesian Optimization of Hyperparameters Using Gaussian Processes

, 2019
The goal of this thesis was to implement a practical tool for optimizing hy- perparameters of neural networks using Bayesian optimization. We show the theoretical foundations of Bayesian optimization, including the necessary math- ematical background for
Arnold, Jakub
core  

Hyperparameters optimization of evolving spiking neural network using artificial bee colony for unsupervised anomaly detection

Journal of Intelligent Systems
Nowadays, anomaly detection in streaming data has gained considerable attention due to the exponential growth in the data gathered by Internet of Things applications. Analyzing and processing vast data volumes requires a system capable of working in real-
Rehan Rabie, Sahran Shahnorbanun, Alyasseri Zaid Abdi Alkareem, Sani Nor Samsiah, Al-Betar Mohammed Azmi   +4 more
doaj   +1 more source