Results 31 to 40 of about 6,098 (213)

Comparing Automated Machine Learning Against an Off-the-Shelf Pattern-Based Classifier in a Class Imbalance Problem: Predicting University Dropout

IEEE Access, 2023
When facing a classification problem, data science practitioners must search through an armory of methods. Often, practitioners are tempted to use off-the-shelf classifiers, including automated Machine Learning (AutoML) toolboxes; however, stand-alone ...
Leonardo Canete-Sifuentes, Victor Robles, Ernestina Menasalvas, Raul Monroy   +3 more
doaj   +1 more source

Comparison of automl methods.

, 2021
Comparison of automl methods.
Fenfen Liu (11520229), Gengpei Zhang (10131572), Guangyuan Ge (11520226)   +2 more
core   +1 more source

AutoML for estimating grass height from ETM+/OLI data from field measurements at a nature reserve

GIScience & Remote Sensing, 2022
Remote sensing (RS) is now a standard tool used for grassland monitoring thanks to the availability of data at an unprecedented spatial and temporal resolution. The approaches to monitor grasslands often rely on the use of vegetation indices (e.g.
Nuno César de Sá, Mitra Baratchi, Vincent Buitenhuis, Perry Cornelissen, Peter M. van Bodegom   +4 more
doaj   +1 more source

Comparison of AutoML specifications.

, 2022
Comparison of AutoML specifications.
Nam K. Tran (9688342), Karina Klein (13175993), Michelle R. Salemi (225784), John Pepper (6553169), Samer Albahra (13175996), Taylor Howard (13175990), Larissa May (5320640), Hooman H. Rashidi (4621522), Brett Phinney (2083123)   +8 more
core   +1 more source

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning [PDF]

, 2022
Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success.
Hutter, Frank, Falkner, Stefan, Feurer, Matthias, Lindauer, Marius, Eggensperger, Katharina   +4 more
core   +4 more sources

Automatic Machine Learning (AutoML)

, 2015
International audienceThe success of machine learning in many domains crucially relies on human machine learning experts, who select appropriate features, workflows, machine learning paradigms, algorithms, and their hyperparameters.
Guyon, I., Viegas, E., Kégl, Balázs, Hutter, F., Larochelle, H., Caruana, R.   +5 more
core   +3 more sources

Deep Pipeline Embeddings for AutoML

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
9 ...
Sebastian Pineda-Arango, Josif Grabocka
openaire   +2 more sources

Replacing the Ex-Def Baseline in AutoML by Naive AutoML [PDF]

, 2021
Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset.
Mohr, Felix, Wever, Marcel
core  

Development and Applicability Assessment of an Hourly Water Level Prediction Model for Agricultural Reservoirs Using Automated Machine Learning [PDF]

대한환경공학회지
Objectives This study aims to develop and evaluate hourly water level prediction models for agricultural reservoirs using an automated machine learning (AutoML) approach, specifically, employing TPOT.
Joonyoung Choi, Bong-Kuk Lee, Jeongho Han   +2 more
doaj   +1 more source

Automl Systems for Medical Imaging

, 2023
11 pages, 4 figures; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging"
Tasmia Tahmida Jidney, Angona Biswas, Md Abdullah Al Nasim, Ismail Hossain, Md Jahangir Alam 0003, Sajedul Talukder, Mofazzal Hossain, Md. Azim Ullah   +7 more
openaire   +3 more sources