Generating Neural Archetypes to Instruct Fast and Interpretable Decisions

DOI10.1007/978-3-030-38227-8_6zbMath1442.62766OpenAlexW3005375288MaRDI QIDQ5114913

Giansalvo Cirrincione, Gabriele Ciravegna, Pietro Barbiero, Giovanni Squillero, Alberto Tonda

Publication date: 29 June 2020

Published in: Advances in Intelligent Systems and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-38227-8_6

zbMATH Keywords

archetypesclassificationneural networkshierarchical clusteringself-organizationmachine learningbig datasemi-supervised learningcoresetsexplain AIGH-ARCH

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Neural nets and related approaches to inference from stochastic processes (62M45) Compound decision problems in statistical decision theory (62C25) Statistical aspects of big data and data science (62R07)

Uses Software

• Scikit
• OpenML

Cites Work

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• Least angle regression. (With discussion)
• The growing curvilinear component analysis (GCCA) neural network
• Near-Optimal Coresets for Least-Squares Regression
• Random forests

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