New support vector algorithms with parametric insensitive/margin model

DOI10.1016/J.NEUNET.2009.08.001zbMath1396.68092DBLPjournals/nn/Hao10OpenAlexW2032023325WikidataQ51797212 ScholiaQ51797212MaRDI QIDQ1784537

Pei-Yi Hao

Publication date: 27 September 2018

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2009.08.001

zbMATH Keywords

classificationregression estimationsupport vector machines (SVMs)heteroscedastic noise modelparametric-insensitive modelparametric-margin model

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (15)

\(L_{1}\)-norm loss based twin support vector machine for data recognition ⋮ Multi-output parameter-insensitive kernel twin SVR model ⋮ PTSVRs: regression models via projection twin support vector machine ⋮ Scaling up minimum enclosing ball with total soft margin for training on large datasets ⋮ Relabeling noisy labels: a Twin SVM approach ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Unnamed Item ⋮ General twin support vector machine with pinball loss function ⋮ Support vector classification with fuzzy hyperplane ⋮ Improving prediction models applied in systems monitoring natural hazards and machinery ⋮ TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition ⋮ Efficient implicit Lagrangian twin parametric insensitive support vector regression via unconstrained minimization problems ⋮ D.C. programming for sparse proximal support vector machines ⋮ An improvement on parametric \(\nu\)-support vector algorithm for classification

Uses Software

• UCI-ml

Cites Work

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• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Support vector interval regression networks for interval regression analysis.
• Doubly penalized likelihood estimator in heteroscedastic regression
• Predictive uncertainty in environmental modelling
• Support vector interval regression machine for crisp input and output data
• Choosing multiple parameters for support vector machines

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