Efficient optimization of hyper-parameters for least squares support vector regression

DOI10.1080/10556788.2015.1025133zbMath1328.65136OpenAlexW2106081954MaRDI QIDQ3458833

Klaus Luig, Nico Strasdat, Thorsten Thies, Gerd Langensiepen, Andreas Fischer

Publication date: 28 December 2015

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2015.1025133

zbMATH Keywords

model selectionbilevel optimizationsupport vector regressionhyper-parameter

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Applications of mathematical programming (90C90) Nonlinear programming (90C30) Learning and adaptive systems in artificial intelligence (68T05) Sensitivity, stability, parametric optimization (90C31) Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33)

Related Items (1)

A Benson-type algorithm for bounded convex vector optimization problems with vertex selection

Cites Work

• A nested heuristic for parameter tuning in support vector machines
• Foundations of bilevel programming
• Practical selection of SVM parameters and noise estimation for SVM regression
• Optimal parameter selection in support vector machines
• Leave-One-Out Bounds for Support Vector Regression Model Selection
• Classification model selection via bilevel programming
• Choosing multiple parameters for support vector machines

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