Erind Bedalli

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Semi-supervised learning is an eminent domain of machine learning focusing on real-life problems where the labeled data instances are scarce. This paper innovatively extends existing factorization models into a supervised nonlinear factorization. The current state of the art methods for semi-supervised regression are based on supervised manifold(More)
Clustering is a very useful technique which helps to enrich the semantics of the data by revealing patterns in large collections of poly-dimensional data. Moreover the fuzzy approach in clustering provides flexibility and enhanced modeling capability, as the results are expressed in soft clusters, allowing partial memberships of data points in the clusters.(More)
— The goal of clustering algorithms is to reveal patterns by partitioning the data into clusters, based on the similarity of the data, without any prior knowledge. The fuzzy approach to the clustering problem, where the fuzzy c-means clustering algorithm (FCM) is one of the eminent representatives, provides more flexibility as it allows data to have partial(More)
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