Model selection for the LS-SVM. Application to handwriting recognition

DOI10.1016/J.PATCOG.2008.10.023zbMath1187.68424OpenAlexW2084136177MaRDI QIDQ733074

Mathias M. Adankon, Mohamed Cheriet

Publication date: 15 October 2009

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2008.10.023

zbMATH Keywords

model selectionsupport vector machinekernel machineLS-SVM

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (13)

CTSVM: a robust twin support vector machine with correntropy-induced loss function for binary classification problems ⋮ Efficient sparse least squares support vector machines for pattern classification ⋮ Least squares twin multi-class classification support vector machine ⋮ Structural nonparallel support vector machine for pattern recognition ⋮ Attribute reduction based on consistent covering rough set and its application ⋮ Using robust dispersion estimation in support vector machines ⋮ Linear Classification of Data with Support Vector Machines and Generalized Support Vector Machines ⋮ Linear and Nonlinear Classifiers of Data with Support Vector Machines and Generalized Support Vector Machines ⋮ Adaptive tangent distance classifier on recognition of handwritten digits ⋮ Design of a multiple kernel learning algorithm for LS-SVM by convex programming ⋮ An online core vector machine with adaptive MEB adjustment ⋮ Nonlinear Analysis and Attitude Control of a Gyrostat Satellite with Chaotic Dynamics Using Discrete-Time LQR-OGY ⋮ A novel quality prediction method based on feature selection considering high dimensional product quality data

Cites Work

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• Benchmarking least squares support vector machine classifiers
• Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
• Optimizing resources in model selection for support vector machine
• Improved sparse least-squares support vector machines
• On a class of pattern recognition learning algorithms
• Efficient computations for large least square support vector machine classifiers
• Chaos control using least-squares support vector machines
• Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
• Radius Margin Bounds for Support Vector Machines with the RBF Kernel
• Choosing multiple parameters for support vector machines

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