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Robust linear programming discrimination of two linearly inseparable sets

Bennett, Kristin P., Mangasarian, O. L.·1992·Optimization Methods and Software

Abstract

A single linear programming formulation is proposed which generates a plane that of minimizes an average sum of misclassified points belonging to two disjoint points sets in n-dimensional real space. When the convex hulls of the two sets are also disjoint, the plane completely separates the two sets. When the convex hulls intersect, our linear program, unlike all previously proposed linear programs, is guaranteed to generate some error-minimizing plane, without the imposition of extraneous normalization constraints that inevitably fail to handle certain cases. The effectiveness of the proposed linear program has been demonstrated by successfully testing it on a number of databases. In addition, it has been used in conjunction with the multisurface method of piecewise-linear separation to train a feed-forward neural network with a single hidden layer.

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Metadata

Authors
Bennett, K. P., & Mangasarian, O. L.
Venue
Optimization Methods and Software
Published
1992-01
Volume
1
Issue
1
Pages
23-34
ISSN
  • 1026-7670 (ISSN-L)
  • 1055-6788
  • 1029-4937
OpenAlex
W2051381803
Cited by
780
References
0
FWCI
7.15
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Robust linear programming discrimination of two linearly inseparable sets. https://app.cochat.ai/papers/10.1080/10556789208805504
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Bennett, K. P., & Mangasarian, O. L. (1992). Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software. https://doi.org/10.1080/10556789208805504

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.