On k-hulls and related problems
SIAM Journal on Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applications of random sampling in computational geometry, II
Discrete & Computational Geometry - Selected papers from the fourth ACM symposium on computational geometry, Univ. of Illinois, Urbana-Champaign, June 6 8, 1988
Algorithms for weak and wide separation of sets
Discrete Applied Mathematics
k-Violation linear programming
Information Processing Letters
On a class of O(n2) problems in computational geometry
Computational Geometry: Theory and Applications
Linear Programming in Linear Time When the Dimension Is Fixed
Journal of the ACM (JACM)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Separating objects in the plane by wedges and strips
Discrete Applied Mathematics - Special issue 14th European workshop on computational geometry CG'98 Selected papers
Fast penetration depth computation for physically-based animation
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Taking a Walk in a Planar Arrangement
SIAM Journal on Computing
Penetration depth of two convex polytopes in 3D
Nordic Journal of Computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Incremental Penetration Depth Estimation between Convex Polytopes Using Dual-Space Expansion
IEEE Transactions on Visualization and Computer Graphics
Low-Dimensional Linear Programming with Violations
SIAM Journal on Computing
On the complexity of working set selection
Theoretical Computer Science
On Approximating the Depth and Related Problems
SIAM Journal on Computing
Bichromatic separability with two boxes: A general approach
Journal of Algorithms
A dynamic data structure for 3-D convex hulls and 2-D nearest neighbor queries
Journal of the ACM (JACM)
Journal of Computer and System Sciences
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Given linearly inseparable sets R of red points and B of blue points, we consider several measures of how far they are from being separable. Intuitively, given a potential separator (''classifier''), we measure its quality (''error'') according to how much work it would take to move the misclassified points across the classifier to yield separated sets. We consider several measures of work and provide algorithms to find linear classifiers that minimize the error under these different measures.