On a Scalable Entropic Breaching of the Overfitting Barrier for Small Data Problems in Machine Learning

DOI10.1162/neco_a_01296zbMath1473.68146arXiv2002.03176OpenAlexW3033186130WikidataQ96293568 ScholiaQ96293568MaRDI QIDQ5131158

Illia Horenko

Publication date: 2 November 2020

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.03176

Mathematics Subject Classification ID

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

Uses Software

• t-SNE

Cites Work

• Data-driven model reduction and transfer operator approximation
• Principal component analysis.
• Least angle regression. (With discussion)
• Information Theory and Statistical Mechanics
• Large-Scale Machine Learning with Stochastic Gradient Descent
• Learning latent block structure in weighted networks
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution
• Handbook of stochastic methods for physics, chemistry and the natural sciences.
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