Results 51 to 60 of about 1,858,266 (298)
Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization [PDF]
E3S Web of Conferences, 2023 There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations.
Muliawan Nicholas Hans +2 more
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Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization
HeliyonThe immense popularity of convolutional neural network (CNN) models has sparked a growing interest in optimizing their hyperparameters. Discovering the ideal values for hyperparameters to achieve optimal CNN training is a complex and time-consuming task,
Mikolaj Wojciuk +3 more
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Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks
IEEE Access, 2021 This paper aims to automatically design optimal LSTM topologies using the clonal selection algorithm (CSA) to solve text classification tasks such as sentiment analysis and SMS spam classification.
Ali Al Bataineh, Devinder Kaur
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Metalearning for Hyperparameter Optimization [PDF]
, 2022 SummaryThis chapter describes various approaches for the hyperparameter optimization (HPO) and combined algorithm selection and hyperparameter optimization problems (CASH). It starts by presenting some basic hyperparameter optimization methods, including grid search, random search, racing strategies, successive halving and hyperband. Next, it discusses
Brazdil, Pavel +3 more
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Automatic Termination for Hyperparameter Optimization
, 2021 Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget,
Makarova, Anastasia +7 more
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Bayesian optimization of hyperparameters from noisy marginal likelihood estimates
, 2023 Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. Bayesian optimization is an iterative method where a Gaussian process posterior of the underlying function is sequentially updated by new ...
Mattias Villani +6 more
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Theoretical Aspects in Penalty Hyperparameters Optimization
Mediterranean Journal of Mathematics, 2023 AbstractLearning processes play an important role in enhancing understanding and analyzing real phenomena. Most of these methodologies revolve around solving penalized optimization problems. A significant challenge arises in the choice of the penalty hyperparameter, which is typically user-specified or determined through Grid search approaches.
Esposito F., Selicato L., Sportelli C.
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Overtuning in Hyperparameter Optimization
CoRRAccepted at the Fourth Conference on Automated Machine Learning (Methods Track).
Lennart Schneider +2 more
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Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
Applied Sciences, 2022 Recommendation attack attempts to bias the recommendation results of collaborative recommender systems by injecting malicious ratings into the rating database. A lot of methods have been proposed for detecting such attacks.
Quanqiang Zhou +2 more
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Predictive Value of Composite Inflammatory Markers for Stroke Prognosis: A Prospective Cohort Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Novel composite inflammatory markers' role in stroke prognosis is understudied, and the best predictor is unclear, requiring further exploration. Objectives This study aimed to systematically evaluate the associations of 6 novel composite inflammatory markers on stroke prognosis.
Bing Wu +7 more
wiley +1 more source