Now showing items 1-10 of 30
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 ...
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.
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.
Analysis of Spanish text-thesaurus as a complex network
[EN]Based on the theoretical tools of Complex Networks, this work provides a basic descriptive study of a synonyms dictionary, the Spanish Open Thesaurus represented as a graph. We study the main structural measures of the ...
New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination
Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. ...
The Linear Ordering Problem Revisited
The Linear Ordering Problem is a popular combinatorial optimisation problem which has been extensively addressed in the literature. However, in spite of its popularity, little is known about the characteristics of this ...
Using network mesures to test evolved NK-landscapes
In this paper we empirically investigate which are the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms. To this end, we evolve instances ...
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 ...
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 ...