Results 241 to 250 of about 131,851 (273)

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
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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   +1 more source

Model-based hyperparameter optimization

2023
L’objectif principal de ce travail est de proposer une méthodologie de découverte des hyperparamètres. Les hyperparamètres aident les systèmes à converger lorsqu’ils sont bien réglés et fabriqués à la main. Cependant, à cette fin, des hyperparamètres mal choisis laissent les praticiens dans l’incertitude, entre soucis de mise en oeuvre ou mauvais choix
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Results for "Optimizing hyperparameters"

2020
Output from optimizing hyperparmeters, find scripts on https://github.com/asreview/paper-optimizing ...
van de Schoot, Rens, de Bruin, Jonathan, Ferdinands, Gerbrich   +2 more
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Gradient-Based Optimization of Hyperparameters

Neural Computation, 2000
Many machine learning algorithms can be formulated as the minimization of a training criterion that involves a hyperparameter. This hyperparameter is usually chosen by trial and error with a model selection criterion. In this article we present a methodology to optimize several hyper-parameters, based on the computation of the gradient of a model ...
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Hyperparameter optimization in learning systems

Journal of Membrane Computing, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Advancing hyperparameter optimization

Hyperparameter optimization (HPO) is a fundamental aspect of machine learning (ML), directly influencing model performance and adaptability. As a computationally expensive black-box optimization problem, HPO requires efficient algorithms to identify optimal hyperparameter configurations.
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Hyperparameter Optimization

2023
Marc Becker, Lennart Schneider, Sebastian Fischer   +2 more
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Surrogate-Assisted Hybrid-Model Estimation of Distribution Algorithm for Mixed-Variable Hyperparameters Optimization in Convolutional Neural Networks

IEEE Transactions on Neural Networks and Learning Systems, 2023
Jian-Yu Li, Zhi-Hui Zhan, Sam Tak Wu Kwong   +2 more
exaly  

On the use of acquisition function‐based Bayesian optimization method to efficiently tune SVM hyperparameters for structural damage detection

Structural Control and Health Monitoring, 2021
Anand Kumar Agrawal
exaly