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A new good point set stepwise shrinkage optimization in machine learning model for fog node performance prediction. [PDF]

Sci Rep
Bo Z, Hasan MK, Sundararajan EA, Islam S, Zhang P, Ahmed FRA, Babiker NBM.   +6 more
europepmc   +1 more source

Earthquake magnitude estimation using an optimized CNN-DNN-GRU model with binary differential evolution and firefly algorithm. [PDF]

Sci Rep
Ahmed N, Abbas S, Fouad Y, Osman AM, El-Bakry HM, Elshewey AM.   +5 more
europepmc   +1 more source

Machine learning prediction and hybrid GRA-AHP optimization of AWJM parameters of ultrasonic assisted stir cast Al6061-B[Formula: see text]C-ZrO[Formula: see text] composites. [PDF]

Sci Rep
K A, Shaik AS, Tota RK, Chavda NK, Bodduru K, Alqahtani SS, Kassa MK.   +6 more
europepmc   +1 more source

State-Driven Adaptive Deep-Unfolded PGA Algorithm for Hybrid Beamforming in MIMO-JCAS Systems. [PDF]

Sensors (Basel)
Liu F, Wang Z, Gao Y, Yao Z.
europepmc   +1 more source

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Reproducible Hyperparameter Optimization

Journal of Computational and Graphical Statistics, 2021
A key issue in machine learning research is the lack of reproducibility. We illustrate what role hyperparameter search plays in this problem and how regular hyperparameter search methods can lead t...
Lars Hertel, Pierre Baldi, Daniel L. Gillen   +2 more
openaire   +2 more sources

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

Automated machine learning hyperparameters tuning through meta-guided Bayesian optimization

Progress in Artificial Intelligence
International audienceThe selection of one or more optimized Machine Learning (ML) algorithms and the configuration of significant hyperparameters are among the crucial but challenging tasks for the advanced data analytics using ML methodologies. However,
Mourad Bouneffa, Moncef Garouani
exaly   +2 more sources

Learning hyperparameter optimization initializations

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015
Hyperparameter optimization is often done manually or by using a grid search. However, recent research has shown that automatic optimization techniques are able to accelerate this optimization process and find hyperparameter configurations that lead to better models.
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme   +2 more
openaire   +2 more sources

Hyperparameter optimization in learning systems

Journal of Membrane Computing, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources