A new polynomial-time algorithm for linear programming
Combinatorica
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
Solving linear inequalities in a least squares sense
SIAM Journal on Scientific Computing - Special issue on iterative methods in numerical linear algebra; selected papers from the Colorado conference
Multisurface method of pattern separation
IEEE Transactions on Information Theory
Polyhedral separability through successive LP
Journal of Optimization Theory and Applications
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximizing the Margin with Boosting
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
A Feature Selection Newton Method for Support Vector Machine Classification
Computational Optimization and Applications
Boosting as a Regularized Path to a Maximum Margin Classifier
The Journal of Machine Learning Research
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometrical Properties of Nu Support Vector Machines with Different Norms
Neural Computation
Efficient Margin Maximizing with Boosting
The Journal of Machine Learning Research
Fuzzy hyper-prototype clustering
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Support vector machines for classification of input vectors with different metrics
Computers & Mathematics with Applications
Subspace embeddings for the L1-norm with applications
Proceedings of the forty-third annual ACM symposium on Theory of computing
Generalized projections onto convex sets
Journal of Global Optimization
Review: Supervised classification and mathematical optimization
Computers and Operations Research
A use of DEA-DA to measure importance of R&D expenditure in Japanese information technology industry
Decision Support Systems
A pure L1-norm principal component analysis
Computational Statistics & Data Analysis
Column Generation for the Minimum Hyperplanes Clustering Problem
INFORMS Journal on Computing
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A plane separating two point sets in n-dimensional real space is constructed such that it minimizes the sum of arbitrary-norm distances of misclassified points to the plane. In contrast to previous approaches that used surrogates for distance-minimization, the present work is based on a precise norm-dependent explicit closed form for the projection of a point on a plane. This projection is used to formulate the separating-plane problem as a minimization of a convex function on a unit sphere in a norm dual to that of the arbitrary norm used. For the 1-norm, the problem can be solved in polynomial time by solving 2n linear programs or by solving a bilinear program. For a general p-norm, the minimization problem can be transformed via an exact penalty formulation to minimizing the sum ofa convex function and a bilinear function on a convex set. For the one and infinity norms, a finite successive linearization algorithm can be used for solving the exact penalty formulation.