Abstract. The aim of this work is to forecast future events in financial data sets, in particular, we focus our attention on situations where positive instances are rare, which fal...
Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...
In this paper, we present new probabilistic models for identifying bird species from audio recordings. We introduce the independent syllable model and consider two ways of aggregat...
We introduce a new framework for the automatic selection of the best views of 3D models. The approach is based on the assumption that models belonging to the same class of shapes ...
We discuss multiclass-multilabel classification problems in which the set of classes is extremely large. Most existing multiclass-multilabel learning algorithms expect to observe ...