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Found 3 papers, 1 papers with code
Feature Selection and Hyperparameter Fine-tuning in Artificial Neural Networks for Wood Quality Classification
The predictive performance of the model was compared against five baseline methods as well as a random search, performing either ANN hyperparameter tuning and feature selection.
Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters
Our approach is grounded on a small set of optimized values able to obtain predictive performance values better than default settings provided by popular tools.
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers
For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them.
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