Classifiers in almost empty spaces

Recent developments in defining and training statistical classifiers make it possible to build reliable classifiers in very small sample size problems. Using these techniques advanced problems may be tackled, such as pixel based image recognition and dissimilarity based object classification. It can be explained and illustrated how recognition systems based on support vector machines and subspace classifiers circumvent the curse of dimensionality, and even may find nonlinear decision boundaries for small training sets represented in Hilbert space.

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