Learning to classify with missing and corrupted features

DOI10.1007/S10994-009-5124-8zbMath1470.68095OpenAlexW2611675901MaRDI QIDQ1959617

Lin Xiao, Ofer Dekel, Ohad Shamir

Publication date: 7 October 2010

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

Full work available at URL: https://doi.org/10.1007/s10994-009-5124-8

zbMATH Keywords

binary classificationadversarial environmentdeleted features

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Linear programming (90C05)

Related Items (7)

Analysis of classifiers' robustness to adversarial perturbations ⋮ Comparison of imputation methods for discriminant analysis with strategically hidden data ⋮ Supervised classification and mathematical optimization ⋮ Dropout training for SVMs with data augmentation ⋮ Learning to classify with missing and corrupted features ⋮ Optimization problems for machine learning: a survey ⋮ Pointed subspace approach to incomplete data

Uses Software

• UCI-ml

Cites Work

• Compact vs. exponential-size LP relaxations
• Learning to classify with missing and corrupted features
• On the Generalization Ability of On-Line Learning Algorithms
• Learning Theory and Kernel Machines
• The elements of statistical learning. Data mining, inference, and prediction
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