Noise modelling and evaluating learning from examples

DOI10.1016/0004-3702(94)00094-8zbMath1506.68095OpenAlexW2079788575MaRDI QIDQ2674201

Ray J. Hickey

Publication date: 22 September 2022

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0004-3702(94)00094-8

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Asymmetric Error Control Under Imperfect Supervision: A Label-Noise-Adjusted Neyman–Pearson Umbrella Algorithm ⋮ A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise ⋮ Anomaly and novelty detection for robust semi-supervised learning ⋮ Core clustering as a tool for tackling noise in cluster labels

Uses Software

• UCI-ml

Cites Work

• Unnamed Item
• Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
• Noise-tolerant Occam algorithms and their applications to learning decision trees
• Very simple classification rules perform well on most commonly used datasets
• Majorisation, randomness and some discrete distributions
• Continuous majorisation and randomness
• A note on the measurement of randomness
• A theory of the learnable
• Inequalities: theory of majorization and its applications

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