Support vector regression with noisy data: a second order cone programming approach

DOI10.1080/03081070601058760zbMath1118.68127OpenAlexW2115814713MaRDI QIDQ3426359

Theodore B. Trafalis, Samir Alwazzi

Publication date: 8 March 2007

Published in: International Journal of General Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03081070601058760

zbMATH Keywords

optimizationrobustnessparameter estimationregressionsupport vector machineskernel functions

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Convex programming (90C25) Learning and adaptive systems in artificial intelligence (68T05) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (max. 100)

Support vector regression for polyhedral and missing data ⋮ SVM Classification of Uncertain Data Using Robust Multi-Kernel Methods

Uses Software

• VanHuffel

Cites Work

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• An Analysis of the Total Least Squares Problem
• Applications of second-order cone programming
• Robust solutions of uncertain linear programs
• Lectures on Modern Convex Optimization
• Robust Convex Optimization
• Robust Solutions to Least-Squares Problems with Uncertain Data
• Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones

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