Geometrical Complexity of Classification Problems

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dc.creatorHo, Tin Kam
dc.date2004-02-11
dc.date.accessioned2026-07-07T03:20:53Z
dc.date.available2026-07-07T03:20:53Z
dc.descriptionDespite encouraging recent progresses in ensemble approaches, classification methods seem to have reached a plateau in development. Further advances depend on a better understanding of geometrical and topological characteristics of point sets in high-dimensional spaces, the preservation of such characteristics under feature transformations and sampling processes, and their interaction with geometrical models used in classifiers. We discuss an attempt to measure such properties from data sets and relate them to classifier accuracies.
dc.descriptionProceedings of the 7th Course on Ensemble Methods for Learning Machines at the International School on Neural Nets ``E.R. Caianiello''
dc.identifierhttps://arxiv.org/abs/cs/0402020
dc.identifierhttp://arxiv.org/abs/cs/0402020
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31991
dc.subjectComputer Vision and Pattern Recognition
dc.subjectI.5.0
dc.titleGeometrical Complexity of Classification Problems
dc.typetext

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