Data complexity meta-features for regression problems

DOI10.1007/s10994-017-5681-1zbMath1462.62392DBLPjournals/ml/LorenaMMCP18OpenAlexW2777539043WikidataQ62764519 ScholiaQ62764519MaRDI QIDQ142808

Ivan G. Costa, Ana C. Lorena, Ricardo B. C. Prudêncio, Aron I. Maciel, Péricles B. C. De Miranda, Ricardo B. C. Prudêncio, Ivan G. Costa, Péricles B. C. de Miranda, Aron I. Maciel, Ana Carolina Lorena

Publication date: 21 December 2017

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-017-5681-1

zbMATH Keywords

complexity measuresmeta-learningmeta-features

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

AutonoML: Towards an Integrated Framework for Autonomous Machine Learning ⋮ A Comparison of Robust Model Choice Criteria Within a Metalearning Study ⋮ ECoL

Uses Software

• UCI-ml
• Scikit
• Auto-WEKA

Cites Work

• Unnamed Item
• A meta-learning method to select the kernel width in support vector regression
• Metalearning
• Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

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