Although being a crucial question for the development of machine learning algorithms, there
is still no consensus on how to compare classifiers over multiple data sets with respect to …

We propose a framework for descriptively analyzing sets of partial orders based on the
concept of depth functions. Despite intensive studies of depth functions in linear and metric …

We propose a framework for descriptively analyzing sets of partial orders based on the
concept of depth functions. Despite intensive studies in linear and metric spaces, there is …

Given the vast number of classifiers that have been (and continue to be) proposed, reliable
methods for comparing them are becoming increasingly important. The desire for reliability …

“It is often said that mathematics is a language. If so, group theory provides the proper
vocabulary for discussing symmetry. In the same way, lattice theory provides the proper …

Several topics in approximation and classification with perturbed data are discussed,
including problems of function approximation with corrupted data, classification with …

A novel correction algorithm is proposed for multi-class classification problems with
corrupted training data. The algorithm is non-intrusive, in the sense that it post-processes a …