Abstrakt
The classification task for data coming from certain subspaces of continuous functions are discussed. This functions are of noisy nature and no further assumptions about the distributions are stated.
Special attention is paid to depth-based classification and its possible generalizations. Several established depth functionals are compared.
The outcoming drawbacks of these methods are fixed by considering the derivatives of the smoothed versions of functions, although the observations don't have to be differentiable itself.