Results 241 to 250 of about 218,805 (280)

Introduction to Hyperparameters

2020
Artificial intelligence (AI) is suddenly everywhere, transforming everything from business analytics, the healthcare sector, and the automobile industry to various platforms that you may enjoy in your day-to-day life, such as social media, gaming, and the wide spectrum of the entertainment industry.
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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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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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Data Dependent Hyperparameter Assignment

1997
We show that in supervised learning from a particular data set Bayesian model selection, based on the evidence, does not optimise generalization performance even for a learnable linear problem. This is achieved by examining the finite size effects in hyperparameter assignment from the evidence procedure and its effect on generalisation.
Glenn Marion, David Saad
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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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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  

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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How to tune the RBF SVM hyperparameters? An empirical evaluation of 18 search algorithms

Artificial Intelligence Review, 2021
Jacques Wainer, Pablo Fonseca
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