An iterative algorithm learning the maximal margin classifier.

DOI10.1016/S0031-3203(03)00060-8zbMath1045.68115OpenAlexW2030506963MaRDI QIDQ1400483

Vojtěch Franc, Václav Hlaváč

Publication date: 13 August 2003

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

Full work available at URL: https://doi.org/10.1016/s0031-3203(03)00060-8

zbMATH Keywords

Support vector machinesPattern recognitionSupervised learningKernel functionsLinear classifier

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (9)

A \(\nu \)-twin support vector machine (\(\nu \)-TSVM) classifier and its geometric algorithms ⋮ Linear convergence rate for the MDM algorithm for the nearest point problem ⋮ Clipping algorithms for solving the nearest point problem over reduced convex hulls ⋮ Class imbalance learning using fuzzy ART and intuitionistic fuzzy twin support vector machines ⋮ An effective method to determine whether a point is within a convex hull and its generalized convex polyhedron classifier ⋮ A general soft method for learning SVM classifiers with \(L_{1}\)-norm penalty ⋮ A theorem on strict separability of convex polyhedra and its applications in optimization ⋮ Efficient implicit Lagrangian twin parametric insensitive support vector regression via unconstrained minimization problems ⋮ CCH-based geometric algorithms for SVM and applications

Uses Software

• Matlab
• SVMlight

Cites Work

• Ten lectures on statistical and structural pattern recognition.
• Theoretical foundations of the potential function method in pattern recognition learning
• Finding the Point of a Polyhedron Closest to the Origin
• An Iterative Procedure for Computing the Minimum of a Quadratic Form on a Convex Set
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