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An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version).

, , , , , , и . CoRR, (2020)

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Mauro Mantovani

Dario Mantovani

Fernanda Ramos Gomes

Ana Maria Gomes Capelo Carregal Queirós

Virginia Gomes dos Santos

 

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A meta-learning approach for selecting image segmentation algorithm., , , , , и . Pattern Recognit. Lett., (2019)Time-Series in Hyper-parameter Initialization of Machine Learning Techniques., , и . IDEAL, том 13113 из Lecture Notes in Computer Science, стр. 246-258. Springer, (2021)Exploring Autoencoders for Feature Extraction in Multi-Target Classification., , и . IJCNN, стр. 1-8. IEEE, (2021)A Meta-Learning Approach for Recommendation of Image Segmentation Algorithms., , и . SIBGRAPI, стр. 370-377. IEEE Computer Society, (2016)Effects of Random Sampling on SVM Hyper-parameter Tuning., , и . ISDA, том 557 из Advances in Intelligent Systems and Computing, стр. 268-278. Springer, (2016)AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification., , и . BRACIS (2), том 14196 из Lecture Notes in Computer Science, стр. 291-306. Springer, (2023)Transfer Learning for Algorithm Recommendation., , , , и . CoRR, (2019)An empirical study on hyperparameter tuning of decision trees., , , , , и . CoRR, (2018)To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning., , , , и . IJCNN, стр. 1-8. IEEE, (2015)Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms., , и . CBMS, стр. 50-53. IEEE Computer Society, (2015)