89 citations · 89 across the 4 of their papers we have counts for
6 papers · 1 filter
Learning Abstract Task Representations
Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani +1
A proper form of data characterization can guide the process of learning-algorithm selection and model-performance estimation. The field of meta-learning has provided a rich body o…
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)
Márcio P. Basgalupp, Rodrigo C. Barros, Alex G. C. de Sá +4
This paper presents an experimental comparison among four Automated Machine Learning (AutoML) methods for recommending the best classification algorithm for a given input dataset.…
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…
Towards meta-learning for multi-target regression problems
Gabriel Jonas Aguiar, Everton José Santana, Saulo Martiello Mastelini +2
Several multi-target regression methods were devel-oped in the last years aiming at improving predictive performanceby exploring inter-target correlation within the problem. Howeve…
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…