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Indoor Crowd 3D Localization in Big Buildings from Wi-Fi Access Anonymous Data
(MDPI, 2019-09-27)
Indoor crowd localization and counting in big public buildings pose problems of infrastructure deployment, signal processing, and privacy. Conventional approaches based on optical cameras, either in the visible or infrared ...
Detecting, counting and sizing bluefin tuna schools using medium range sonars of baitboats in the Bay of Biscay.
(2017-07-17)
This study presents a methodology for the automated analysis of commercial medium-range sonar signals for detecting, counting and sizing bluefin tuna (Tunnus thynnus) schools at the Bay of Biscay. Data can be recorded from ...
An R package for permutations, Mallows and Generalized Mallows models
(2014-01-22)
[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
(2010)
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
(2010)
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
(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 ...
K-means for massive data
(2019-04-30)
The K-means algorithm is undoubtedly one of the most popular clustering analysis techniques, due to its easiness in the implementation, straightforward parallelizability and competitive computational complexity, when ...
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 ...
Two Datasets of Defect Reports Labeled By a Crowd of Annotators of Unknown Reliability
(Elsevier, 2018-06)
Classifying software defects according to any defined taxonomy is not straightforward. In order to be used for automatizing the classification of software defects, two sets of defect reports were collected from public issue ...
Theoretical and methodological advances in semi-supervised learning and the class-imbalance problem.
(2018-11-30)
Este trabajo se centra en la generalización teórica y práctica de dos situaciones desafiantes y conocidas del campo del aprendizaje automático a problemas de clasificación en los cuales la suposición de tener una única ...