Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels

DOI10.1016/j.neunet.2016.04.005zbMath1414.68082OpenAlexW2343656455WikidataQ31096112 ScholiaQ31096112MaRDI QIDQ2418195

Anatoly I. Chekh, Yulia A. Zhuk, Lev V. Utkin

Publication date: 3 June 2019

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2016.04.005

zbMATH Keywords

classificationlinear programmingquadratic programminginterval-valued datasupport vector machineminimax strategy

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Robust and distributionally robust optimization models for linear support vector machine ⋮ An improved approximate Bayesian computation scheme for parameter inference based on a recalibration post-processing method

Uses Software

• SNOPT
• UCI-ml

Cites Work

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• Support Vector Machines
• SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
• Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
• Upper and Lower Probabilities Induced by a Multivalued Mapping

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