Results 11 to 20 of about 983,800 (290)

Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer’s Disease With High Performance Computing [PDF]

Frontiers in Artificial Intelligence, 2021
Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased.
Fan Zhang, Fan Zhang, Melissa Petersen, Melissa Petersen, Leigh Johnson, Leigh Johnson, James Hall, James Hall, Sid E. O’Bryant, Sid E. O’Bryant   +9 more
doaj   +2 more sources

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning. [PDF]

BMC Med Inform Decis Mak
Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging.
Vanitha K, R MT, Sree SS, Guluwadi S.
europepmc   +2 more sources

A comparison of hyperparameter tuning procedures for clinical prediction models: A simulation study

Statistics in Medicine
Tuning hyperparameters, such as the regularization parameter in Ridge or Lasso regression, is often aimed at improving the predictive performance of risk prediction models.
Anne-Laure Boulesteix, Dirk Timmerman, Maarten van Smeden   +2 more
exaly   +2 more sources

Refining the ONCE Benchmark With Hyperparameter Tuning

IEEE Access
In 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, Alexander Gambashidze, Ildar Nurgaliev, Ilya Makarov   +3 more
doaj   +4 more sources

Elastic Hyperparameter Tuning on the Cloud [PDF]

Proceedings of the ACM Symposium on Cloud Computing, 2021
Hyperparameter tuning is a necessary step in training and deploying machine learning models. Most prior work on hyperparameter tuning has studied methods for maximizing model accuracy under a time constraint, assuming a fixed cluster size. While this is appropriate in data center environments, the increased deployment of machine learning workloads in ...
Lisa Dunlap, Kirthevasan Kandasamy, Ujval Misra, Richard Liaw, Michael I. Jordan, Ion Stoica, Joseph E. Gonzalez   +6 more
openaire   +2 more sources

Seleksi Fitur dengan Particle Swarm Optimization pada Klasifikasi Penyakit Parkinson Menggunakan XGBoost

Jurnal Teknologi Informasi dan Ilmu Komputer, 2023
Penyakit Parkinson merupakan gangguan pada sistem saraf pusat yang mempengaruhi sistem motorik. Diagnosis penyakit ini cukup sulit dilakukan karena gejalanya yang serupa dengan penyakit lain.
Deni Kurnia, Muhammad Itqan Mazdadi, Dwi Kartini, Radityo Adi Nugroho, Friska Abadi   +4 more
doaj   +3 more sources

Klasifikasi COVID-19 menggunakan Filter Gabor dan CNN dengan Hyperparameter Tuning

Jurnal Elkomika, 2021
ABSTRAK Penyakit COVID-19 dapat timbul karena berbagai faktor sebab dan akibat, sehingga penyakit ini memiliki efek buruk bagi penderita. Pencitraan CT-Scan memiliki keunggulan dalam memproyeksikan kondisi paru-paru pasien penderita, sehingga dapat ...
AGUS EKO MINARNO, MOCHAMMAD HAZMI COKRO MANDIRI, MUHAMMAD RIFAL ALFARIZY   +2 more
doaj   +1 more source

Hyperparameter Tuning

This file contains hyperparameter tuning experiments.
Yiran Chen, Hai Li, Huanrui Yang
  +6 more sources

Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting

IEEE Access, 2021
Stock price forecasting has been reported as a challenging task in the scientific and financial communities due to stock prices’ nonlinear and dynamic nature.
Kazi Ekramul Hoque, Hamoud Aljamaan
doaj   +1 more source

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