Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ameni Filali"'
Publikováno v:
AICCSA
In this study, we introduce a novel hybrid collaboration clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b4e64c31a8311daad19a1f1682ec8e90
https://doi.org/10.1109/aiccsa.2017.111
https://doi.org/10.1109/aiccsa.2017.111
Publikováno v:
2017 14th International Conference on Computer Graphics, Imaging and Visualization.
Feature selection aims to diminish dimensionality for construct comprehensible learning models with good generalization performance. Feature selection methods are mostly studied independently according to the type of learning: supervised or unsupervi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52a175f1132a3f335ee2ad8478b3dbc2
https://doi.org/10.1109/cgiv.2017.18
https://doi.org/10.1109/cgiv.2017.18
Publikováno v:
ICMV
Ensemble learning has succeeded in the growth of stability and clustering accuracy, but their runtime prohibits them from scaling up to real-world applications. This study deals the problem of selecting a subset of the most pertinent features for eve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c7dbae158849c58c88700fba1067fd66
https://doi.org/10.1117/12.2268538
https://doi.org/10.1117/12.2268538
Publikováno v:
ATSIP
In this paper, we focus on collaborative clustering methods based on topological approaches, such as self-organizing maps (SOM) and self-organizing maps based on a locally adapting neighborhood radii (AdSOM). So far, the methods of clustering carried
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d245ce8c52bb4d966aa0fc48189404c
https://doi.org/10.1109/atsip.2016.7523117
https://doi.org/10.1109/atsip.2016.7523117
Publikováno v:
International Journal of Computational Intelligence and Applications. 16:1750004
To uncover an appropriate latent subspace for data representation, we propose in this paper a new extension of the random forests method which leads to the unsupervised feature selection called Feature Selection with Random Forests (RFS) based on SOM
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed090d534aec9c61095b950dc458e5c9
https://doi.org/10.1142/s1469026817500043
https://doi.org/10.1142/s1469026817500043
Publikováno v:
International Journal of Intelligent Systems Technologies and Applications. 16:208
Finding pertinent subspaces in very high-dimensional dataset is a challenging task. The selection of features should be stable, but on the other hand clustering results have to be enhanced. Ensemble methods have successfully increased the stability a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73483c0ec5fe2e8c9827da618ebca499
https://doi.org/10.1504/ijista.2017.085357
https://doi.org/10.1504/ijista.2017.085357