Now showing items 1-10 of 20
An R package for permutations, Mallows and Generalized Mallows models
[EN]Probability models on permutations associate a probability value to each of the permutations on n items. This paper considers two popular probability models, the Mallows model and the Generalized Mallows model. We ...
Learning Bayesian network classifiers for multidimensional supervised classification problems by means of a multiobjective approach
A classical supervised classification task tries to predict a single class variable based on a data set composed of a set of labeled examples. However, in many real domains more than one variable could be considered as a ...
A Preprocessing Procedure for Haplotype Inference by Pure Parsimony
Haplotype data is especially important in the study of complex diseases since it contains more information than genotype data. However, obtaining haplotype data is technically difficult and expensive. Computational methods ...
Efficient learning of decomposable models with a bounded clique size
The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability ...
On the optimal usage of labelled examples in semi-supervised multi-class classification problems
In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a ...
Approaching Sentiment Analysis by Using Semi-supervised Learning of Multidimensional Classifiers
Sentiment Analysis is defined as the computational study of opinions, sentiments and emotions expressed in text. Within this broad field, most of the work has been focused on either Sentiment Polarity classification, ...
Sampling and learning the Mallows and Generalized Mallows models under the Cayley distance
[EN]The Mallows and Generalized Mallows models are compact yet powerful and natural ways of representing a probability distribution over the space of permutations. In this paper we deal with the problems of sampling and ...
Sampling and learning the Mallows and Weighted Mallows models under the Hamming distance
[EN]In this paper we deal with distributions over permutation spaces. The Mallows model is the mode l in use. The associated distance for permutations is the Hamming distance.
Sampling and learning the Mallows model under the Ulam distance
[EN]In this paper we deal with probability distributions over permutation spaces. The Probability model in use is the Mallows model. The distance for permutations that the model uses in the Ulam distance.
A sensitivity study of bias and variance of k-fold cross-validation in prediction error estimation
In the machine learning field the performance of a classifier is usually measured in terms of prediction error. In most real-world problems, the error cannot be exactly calculated and it must be estimated. Therefore, it’s ...