Results 281 to 290 of about 151,967 (341)

Enhancing early-stage breakthrough technology identification: A CatBoost model optimized with the SABO algorithm. [PDF]

PLoS One
Lei M, Yu Y.
europepmc   +1 more source

Undersampling Techniques for Nonlinear Chemical Space Visualization. [PDF]

Mol Inform
Surendran A, Zsigmond K, Miranda-Quintana RA.   +2 more
europepmc   +1 more source

Use of machine learning to detect Escherichia coli in drinking water in Bangladesh. [PDF]

PLoS One
Haq I, Ador MYH, Nobrega D.
europepmc   +1 more source

EcoNet-Multi: optimising deep learning frameworks for robust waste detection in diverse global environments. [PDF]

Front Plant Sci
Shukla AK, Dwivedi S.
europepmc   +1 more source

A study on risk assessment and system development of tunnel lighting facilities based on XGBoost and Bayesian optimization. [PDF]

Sci Rep
Xiao H, Yang W, Tang J, Wang ZL.
europepmc   +1 more source

Convolutional Neural Networks-Based Lung Nodule Classification: A Surrogate-Assisted Evolutionary Algorithm for Hyperparameter Optimization

IEEE Transactions on Evolutionary Computation, 2021
This article investigates deep neural networks (DNNs)-based lung nodule classification with hyperparameter optimization. Hyperparameter optimization in DNNs is a computationally expensive problem, and a surrogate-Assisted evolutionary algorithm has been ...
Juan Lyu, Steven Su, Huiqi Li
exaly   +2 more sources

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Optuna: A Next-generation Hyperparameter Optimization Framework

Knowledge Discovery and Data Mining, 2019
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2 ...
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama   +4 more
semanticscholar   +1 more source

Hyperparameter Optimization Machines

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016
Algorithm selection and hyperparameter tuning are omnipresent problems for researchers and practitioners. Hence, it is not surprising that the efforts in automatizing this process using various meta-learning approaches have been increased. Sequential model-based optimization (SMBO) is ne of the most popular frameworks for finding optimal hyperparameter
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme   +2 more
openaire   +1 more source

Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization

IEEE Congress on Evolutionary Computation, 2021
The performance of machine learning algorithms are affected by several factors, some of these factors are related to data quantity, quality, or its features.
Hussain Alibrahim, Simone A. Ludwig
semanticscholar   +1 more source

Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results

International Journal of Computer Applications, 2021
In this work, we propose hyperparameters optimization using grid search to optimize the parameters of eight existing models and apply the best parameters to predict the outcomes of HIV tests from the Ethiopian Demographic and Health Survey (EDHS), HIV ...
Daniel Mesafint Belete, M. D. Huchaiah
semanticscholar   +1 more source