Search Results for author:
Found 4 papers, 2 papers with code
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.
Towards meta-learning for multi-target regression problems
In this paper, we propose a meta-learning system to recommend the best predictive method for a given multi-target regression problem.
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.
Better Trees: An empirical study on hyperparameter tuning of classification decision tree induction algorithms
DT induction algorithms present high predictive performance and interpretable classification models, though many HPs need to be adjusted.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
