Now showing items 1-8 of 8
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 ...
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 ...
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 ...
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 ...
Extending Distance-based Ranking Models In Estimation of Distribution Algorithms
Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking ...