89 citations · 99 across the 4 of their papers we have counts for
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Dynamic Design of Machine Learning Pipelines via Metalearning
Edesio Alcobaça, André C. P. L. F. de Carvalho
Automated machine learning (AutoML) has democratized the design of machine learning based systems, by automating model selection, hyperparameter tuning and feature engineering. How…
Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters
Rafael Gomes Mantovani, André Luis Debiaso Rossi, Edesio Alcobaça +3
Machine Learning (ML) algorithms have been increasingly applied to problems from several different areas. Despite their growing popularity, their predictive performance is usually…
Transfer Learning for Algorithm Recommendation
Gean Trindade Pereira, Moisés dos Santos, Edesio Alcobaça +2
Meta-Learning is a subarea of Machine Learning that aims to take advantage of prior knowledge to learn faster and with fewer data [1]. There are different scenarios where meta-lear…
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers
Rafael Gomes Mantovani, André Luis Debiaso Rossi, Edesio Alcobaça +2
For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them. However, tuning these hyperparameters can come…