89 citations · 89 across the 4 of their papers we have counts for
5 papers
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.…
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…