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Now showing items 11-20 of 25
Efficient learning of decomposable models with a bounded clique size
(2014-05-08)
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
(2015-04-23)
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
(2011)
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
(2014-01-22)
[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
(2014-01-22)
[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
(2014-01-22)
[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.
Learning Probability Distributions over Permutations by Means of Fourier Coefficients
(2011)
A large and increasing number of data mining domains consider data
that can be represented as permutations. Therefore, it is important to
devise new methods to learn predictive models over datasets of permutations.
However, ...
A review on Estimation of Distribution Algorithms in Permutation-based Combinatorial Optimization Problems
(2011)
Estimation of Distribution Algorithms (EDAs) are a set of algorithms
that belong to the field of Evolutionary Computation. Characterized by the use of
probabilistic models to represent the solutions and the dependencies ...
New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination
(2012-12-27)
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. ...