A novel technique to classify time series with imprecise hidden Markov models is presented.
The learning of these models is achieved by coupling the EM algorithm with the imprecise …

K-nearest neighbours algorithms are among the most popular existing classification
methods, due to their simplicity and good performances. Over the years, several extensions …

Knowledge available through Semantic Web standards can be missing, generally because
of the adoption of the Open World Assumption. We present a Statistical Relational Learning …

We propose a new methodology to classify temporal data with imprecise hidden Markov
models. For each sequence we learn a different model by coupling the EM algorithm with …

This paper proposes a simple framework to combine binary classifiers whose outputs are
imprecise probabilities (or are transformed into some imprecise probabilities, eg, by using …

In this master thesis some properties of bags of imprecise classification trees, as introduced
in Abellán and Masegosa (2010), are analysed. In the beginning the statistical background …

This paper proposes a simple strategy for combining binary classifiers with imprecise
probabilities as outputs. Our combination strategy consists in computing a set of probability …

We propose the COMP-AODE classifier, which adopts the compression-based approach [1]
to average the posterior probabilities computed by different non-naive classifiers (SPODEs) …

Knowledge available through Semantic Web standards can easily be missing, generally
because of the adoption of the Open World Assumption (ie the truth value of an assertion is …

One of the big challenges for science is coping with uncertainty, omnipresent in modern
societies and of ever increasing complexity. Quantitative modelling of uncertainty is …