Hatim Chahdi

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Recent studies have shown that the use of a priori knowledge can significantly improve the results of unsupervised classification. However, capturing and formatting such knowledge as constraints is not only very expensive requiring the sustained involvement of an expert but it is also very difficult because some valuable information can be lost when it(More)
In this paper we introduce a new learning approach, which provides automated topological co-clustering based on Self-Organizing Map. The proposed approach (wd-TCoC) is computationally simple, learns a different feature's weights vector for each prototype (relevance vector) and estimate the data density distribution on the map to produce an automatic(More)
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