Design of a multiple kernel learning algorithm for LS-SVM by convex programming

DOI10.1016/j.neunet.2011.03.009zbMath1217.68185OpenAlexW1988486039WikidataQ51585320 ScholiaQ51585320MaRDI QIDQ553288

Zhonghang Xia, Ling Jian, Chuanhou Gao, Xi-Jun Liang

Publication date: 26 July 2011

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2011.03.009

zbMATH Keywords

quadratically constrained quadratic programmingconvex optimizationsemidefinite programmingleast squares support vector machinesmultiple kernel learning

Mathematics Subject Classification ID

Semidefinite programming (90C22) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Fast prediction with sparse multikernel LS-SVR using multiple relevant time series and its application in avionics system ⋮ Feature selection and multi-kernel learning for sparse representation on a manifold ⋮ Structural multiple empirical kernel learning ⋮ MREKLM: a fast multiple empirical kernel learning machine

Uses Software

• SHOGUN
• SeDuMi
• UCI-ml
• Mosek
• SimpleMKL

Cites Work

• Benchmarking least squares support vector machine classifiers
• Model selection for the LS-SVM. Application to handwriting recognition
• Perturbation analysis of generalized saddle point systems
• Low rank updated LS-SVM classifiers for fast variable selection
• Kernel based support vector machine via semidefinite programming: application to medical diagnosis
• Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
• Chaos control using least-squares support vector machines
• Semidefinite Programming
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