A novel piecewise linear classifier based on polyhedral conic and max-min separabilities

DOI10.1007/s11750-011-0241-5zbMath1263.65051OpenAlexW1971368911MaRDI QIDQ1948524

Gurkan Ozturk, Julien Ugon, Refail Kasimbeyli, Dean Webb, Adil M. Bagirov

Publication date: 23 April 2013

Published in: Top (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11750-011-0241-5

zbMATH Keywords

nonsmooth optimizationsupervised learningdata miningpiecewise linear classifierspiecewise linear separability

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25)

Related Items (12)

Supervised classification and mathematical optimization ⋮ Radiant separation theorems and minimum-type subdifferentials of calm functions ⋮ A polyhedral conic functions based classification method for noisy data ⋮ Optimal arrangements of hyperplanes for SVM-based multiclass classification ⋮ An incremental piecewise linear classifier based on polyhedral conic separation ⋮ Generalized derivatives and optimality conditions in nonconvex optimization ⋮ Nonlinear Cone Separation Theorems in Real Topological Linear Spaces ⋮ Minimum type functions, plus-cogauges, and applications ⋮ Semisupervised spherical separation ⋮ Clustering based polyhedral conic functions algorithm in classification ⋮ O-PCF algorithm for one-class classification ⋮ Support vector machine polyhedral separability in semisupervised learning

Uses Software

• UCI-ml
• DGM

Cites Work

• Automated design of multiple-class piecewise linear classifiers
• Discrete gradient method: Derivative-free method for nonsmooth optimization
• Accurately learning from few examples with a polyhedral classifier
• Piecewise partially separable functions and a derivative-free algorithm for large scale nonsmooth optimization
• Locally Trained Piecewise Linear Classifiers
• A Method for Minimization of Quasidifferentiable Functions
• Separation via polyhedral conic functions
• Polyhedral separability through successive LP
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