Rethinking statistical learning theory: learning using statistical invariants

DOI10.1007/S10994-018-5742-0zbMath1480.62064OpenAlexW2884692446WikidataQ129492950 ScholiaQ129492950MaRDI QIDQ669285

Vladimir Vapnik, Rauf Izmailov

Publication date: 15 March 2019

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

Full work available at URL: https://doi.org/10.1007/s10994-018-5742-0

zbMATH Keywords

classificationweak convergenceneural networklearning theorykernel functionconditional probabilityill-posed problemregressionreproducing kernel Hilbert spacesupport vector machineprivileged informationintelligent teacher

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Computational learning theory (68Q32) Nonparametric estimation (62G05) Learning and adaptive systems in artificial intelligence (68T05) Knowledge representation (68T30)

Related Items (2)

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Uses Software

• UCI-ml

Cites Work

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• Nonparametric methods for reconstructing probability densities
• Knowledge transfer in SVM and neural networks
• Theorie der Zeichenerkennung
• The unreasonable effectiveness of mathematics in the natural sciences. Richard courant lecture in mathematical sciences delivered at New York University, May 11, 1959

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