Introduction to the theory of neural computation
Introduction to the theory of neural computation
Bilinear separation of two sets in n-space
Computational Optimization and Applications
The nature of statistical learning theory
The nature of statistical learning theory
A statistical perspective on knowledge discovery in databases
Advances in knowledge discovery and data mining
Improved generalization via tolerant training
Journal of Optimization Theory and Applications
Feature minimization within decision trees
Computational Optimization and Applications
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Handbook of Neural Computation
Handbook of Neural Computation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Accurately learning from few examples with a polyhedral classifier
Computational Optimization and Applications
Non-smoothness in classification problems
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART I
DC models for spherical separation
Journal of Global Optimization
An aggregate deformation homotopy method for min-max-min problems with max-min constraints
Computational Optimization and Applications
Margin maximization in spherical separation
Computational Optimization and Applications
Particle swarm classification: A survey and positioning
Pattern Recognition
Computers and Industrial Engineering
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We address the problem of discriminating between two finite point sets A and B in the n-dimensional space by h hyperplanes generating a convex polyhedron. If the intersection of the convex hull of A with B is empty, the two sets can be strictly separated (polyhedral separability). We introduce an error function which is piecewise linear, but not convex nor concave, and define a descent procedure based on the iterative solution of the LP descent direction finding subproblems.