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Hierarchical Bottom-Up Safe Semi-Supervised Support Vector Machines for Multi-Class Transductive Learning.

, , , , and . J. Inf. Data Manag., 4 (3): 357-373 (2013)

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Evolving Gaussian Mixture Models with Splitting and Merging Mutation Operators., , and . Evol. Comput., 24 (2): 293-317 (2016)Towards improving cluster-based feature selection with a simplified silhouette filter., and . Inf. Sci., 181 (18): 3766-3782 (2011)Bayesian Feature Selection for Clustering Problems., , , and . J. Inf. Knowl. Manag., 5 (4): 315-327 (2006)A Semi-supervised Approach to Estimate the Number of Clusters per Class., , and . SBRN, page 73-78. IEEE Computer Society, (2012)Splitting and Merging Gaussian Mixture Model Components: An Evolutionary Approach., and . ICMLA (1), page 106-111. IEEE Computer Society, (2011)Feature Selection for Clustering Problems: a Hybrid Algorithm that Iterates Between k-means and a Bayesian Filter., , , and . HIS, page 405-410. IEEE Computer Society, (2005)Fuzzy Clustering-Based Filter., , , and . IPMU (1), volume 80 of Communications in Computer and Information Science, page 406-415. Springer, (2010)Competitive Learning With Pairwise Constraints., , and . IEEE Trans. Neural Networks Learn. Syst., 24 (1): 164-169 (2013)Hierarchical Bottom-Up Safe Semi-Supervised Support Vector Machines for Multi-Class Transductive Learning., , , , and . J. Inf. Data Manag., 4 (3): 357-373 (2013)Feature Selection for Cluster Analysis: an Approach Based on the Simplified Silhouette Criterion., and . CIMCA/IAWTIC, page 32-38. IEEE Computer Society, (2005)