Learning algorithms may perform worse with increasing training set size: algorithm-data incompatibility

DOI10.1016/j.csda.2013.05.021zbMath1506.62203OpenAlexW2028011900MaRDI QIDQ1623460

Waleed A. Yousef, Subrata Kundu

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.05.021

zbMATH Keywords

convergencepattern recognitionmachine learningstatistical learningstable distributionstochastic concentration

Mathematics Subject Classification ID

Infinitely divisible distributions; stable distributions (60E07) Computational methods for problems pertaining to statistics (62-08) Point estimation (62F10) Learning and adaptive systems in artificial intelligence (68T05) Statistical aspects of big data and data science (62R07)

Uses Software

• ElemStatLearn

Cites Work

• Sample size planning for developing classifiers using high-dimensional DNA microarray data
• Linear and nonlinear regression with stable errors
• Multivariate stable densities as functions of one dimensional projections
• Classification rules for stable distributions
• Maximum likelihood estimators in regression models with infinite variance innovations
• Conditional distributions and characterizations of multivariate stable distribution
• Estimating the center of symmetry: is it always better to use larger sample sizes?
• On Dimensionality, Sample Size, Classification Error, and Complexity of Classification Algorithm in Pattern Recognition
• Probability: A Graduate Course
• Density parameter estimation of skewed α-stable distributions
• The elements of statistical learning. Data mining, inference, and prediction
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