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Selection of relevant features and examples in machine learning
Artificial Intelligence 97 (1-2):245-271 (1997)
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Citations of this work
Item response theory in AI: Analysing machine learning classifiers at the instance level.Fernando Martínez-Plumed, Ricardo B. C. Prudêncio, Adolfo Martínez-Usó & José Hernández-Orallo - 2019 - Artificial Intelligence 271 (C):18-42.
Clustering ensemble based on sample's stability.Feijiang Li, Yuhua Qian, Jieting Wang, Chuangyin Dang & Liping Jing - 2019 - Artificial Intelligence 273 (C):37-55.
Consistency-based search in feature selection.Manoranjan Dash & Huan Liu - 2003 - Artificial Intelligence 151 (1-2):155-176.
A Computational Constructivist Model as an Anticipatory Learning Mechanism for Coupled Agent–Environment Systems.F. S. Perotto - 2013 - Constructivist Foundations 9 (1):46-56.
Explanations in AI as Claims of Tacit Knowledge.Nardi Lam - 2022 - Minds and Machines 32 (1):135-158.
References found in this work
Wrappers for feature subset selection.Ron Kohavi & George H. John - 1997 - Artificial Intelligence 97 (1-2):273-324.
Knowing what doesn't matter: exploiting the omission of irrelevant data.Russell Greiner, Adam J. Grove & Alexander Kogan - 1997 - Artificial Intelligence 97 (1-2):345-380.
Dynamic relevance: vision-based focus of attention using artificial neural networks.Shumeet Baluja & Dean Pomerleau - 1997 - Artificial Intelligence 97 (1-2):381-395.
The perceptron algorithm versus winnow: linear versus logarithmic mistake bounds when few input variables are relevant.J. Kivinen, M. K. Warmuth & P. Auer - 1997 - Artificial Intelligence 97 (1-2):325-343.
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