On data classification by iterative linear partitioning

Anthony, Martin

doi:10.1016/j.dam.2004.04.003

Cited by 3 publications

(1 citation statement)

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“…As a third alternative, some authors have proposed classifiers that sequentially generate piecewise linear separating surfaces [9,21]; see also [1] for a theoretical investigation of the generalization properties of these methods. The idea underlying these classifiers is to generate at each step a set of hyperplanes, usually a pair, to separate off a set of examples in R n all having the same class label.…”

Section: Introductionmentioning

confidence: 99%

Accurately learning from few examples with a polyhedral classifier

Orsenigo

Vercellis

2007

Comput Optim Appl

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In the context of learning theory many efforts have been devoted to developing classification algorithms able to scale up with massive data problems. In this paper the complementary issue is addressed, aimed at deriving powerful classification rules by accurately learning from few data. This task is accomplished by solving a new mixed integer programming model that extends the notion of discrete support vector machines, in order to derive an optimal set of separating hyperplanes for binary classification problems. According to the cardinality of the set of hyperplanes, the classification region may take the form of a convex polyhedron or a polytope in the original space where the examples are defined. Computational tests on benchmark datasets highlight the effectiveness of the proposed model, that yields the greatest accuracy when compared to other classification approaches.

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Section: Introductionmentioning

confidence: 99%