Clipping algorithms for solving the nearest point problem over reduced convex hulls

DOI10.1016/j.patcog.2010.08.030zbMath1209.68417OpenAlexW1980351761MaRDI QIDQ621999

Sumit K. Garg

Publication date: 31 January 2011

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2010.08.030

zbMATH Keywords

support vector machinesGSK algorithmMDM algorithmnearest point problemreduced convex hulls

Mathematics Subject Classification ID

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

Related Items (5)

Fast clustering-based weighted twin support vector regression ⋮ \(L_{1}\)-norm loss based twin support vector machine for data recognition ⋮ Linear convergence rate for the MDM algorithm for the nearest point problem ⋮ Scaling up twin support vector regression with safe screening rule ⋮ An effective method to determine whether a point is within a convex hull and its generalized convex polyhedron classifier

Cites Work

• An iterative algorithm learning the maximal margin classifier.
• A general soft method for learning SVM classifiers with \(L_{1}\)-norm penalty
• Dual unification of bi-class support vector machine formulations
• Unified dual for bi-class SVM approaches
• Finding the Point of a Polyhedron Closest to the Origin

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