Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Lallich, Stéphane"'
Publikováno v:
IBaI. 14th International Conference on Machine Learning and Data Mining (MLDM 2018), Jul 2018, New York, United States. Springer, Lecture Notes in Artificial Intelligence, 10934-10935, 2018, Machine Learning and Data Mining in Pattern Recognition. http://www.mldm.de
Clustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to one and only one cluster. Howeve
Externí odkaz:
http://arxiv.org/abs/1808.00197
Publikováno v:
14th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2018), May 2018, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, 519, pp.546-555, 2018, http://easyconferences.eu/aiai2018/
Cluster analysis is widely used in the areas of machine learning and data mining. Fuzzy clustering is a particular method that considers that a data point can belong to more than one cluster. Fuzzy clustering helps obtain flexible clusters, as needed
Externí odkaz:
http://arxiv.org/abs/1806.01552
Data organization is a difficult and essential component in cultural heritage applications. Over the years, a great amount of archaeological ceramic data have been created and processed by various methods and devices. Such ceramic data are stored in
Externí odkaz:
http://arxiv.org/abs/1608.06469
Publikováno v:
Int. J. Artif. Intell. Tools 23, 1460013 (2014) [26 pages]
In this paper, we propose a new time-aware dissimilarity measure that takes into account the temporal dimension. Observations that are close in the description space, but distant in time are considered as dissimilar. We also propose a method to enfor
Externí odkaz:
http://arxiv.org/abs/1601.02603
Publikováno v:
Journal of Intelligent Information Systems, vol. 40, iss. 3, pp. 501-527, 2013
Feature-based format is the main data representation format used by machine learning algorithms. When the features do not properly describe the initial data, performance starts to degrade. Some algorithms address this problem by internally changing t
Externí odkaz:
http://arxiv.org/abs/1512.05467
Publikováno v:
M.-A. Rizoiu, J. Velcin, and S. Lallich, "Semantic-enriched Visual Vocabulary Construction in a Weakly Supervised Context," Intelligent Data Analysis, vol. 19, iss. 1, pp. 161-185, 2015
One of the prevalent learning tasks involving images is content-based image classification. This is a difficult task especially because the low-level features used to digitally describe images usually capture little information about the semantics of
Externí odkaz:
http://arxiv.org/abs/1512.04605
We propose ClusPath, a novel algorithm for detecting general evolution tendencies in a population of entities. We show how abstract notions, such as the Swedish socio-economical model (in a political dataset) or the companies fiscal optimization (in
Externí odkaz:
http://arxiv.org/abs/1512.03501
Autor:
Méry, Benoîte, Froissart, Gilles-Damas, Vallard, Alexis, Lallich, Stéphane, Espenel, Sophie, Jouan, Sarah, Langrand-Escure, Julien, Bagur, Jacques, Chauvin, Fabienne, Ben Mrad, Majed, Ho, Michèle, Bourdis, Marie, Dutilleux, Manuel, Kilendo, Philippe, Michaud, Patrick, de Laroche, Guy, Massoubre, Catherine, Magné, Nicolas
Publikováno v:
In Bulletin du Cancer October 2015 102(10):845-853
Publikováno v:
In Neurocomputing 21 July 2015 160:3-17
Publikováno v:
Revue économique, 1990 May 01. 41(3), 481-500.
Externí odkaz:
https://www.jstor.org/stable/3501864