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The Ontario Research Centre for Computer Algebra
The UWO ORCCA Reading Room |
Abstract:
Many practical problems reduce to classifying curves among multiple
classes, for example on-line recognition of handwritten mathematical
symbols. By treating a curve as an algebraic object and computing
truncated expansions of its parametric coordinate functions in an
orthogonal functional basis, we obtain an accurate, compact, and
geometrically intuitive representation of the curve as a point in a
low-dimensional vector space. Previous work has shown that, with this
representation, high top-k classification rates can be achieved using
support vector machines. However, the gap between the top-1 and top-2
classification accuracies remained large.
We report on a variation of
nearest neighbor classification using the distance to the convex hull of
several nearest neighbors. This reduces the tie-breaking errors
between the top two classes by about 20% and the overall error
rates by about 10%. The technique is well adapted to classification among
hundreds of classes, with the number of training samples ranging from
ten to several thousands, and with strict requirements on the speed
and memory use.
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