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• W2341476366 abstract "Recently semi-supervised methods gained increasing attention and many novel semi-supervised learning algorithms have been proposed. These methods exploit the information contained in the usually large unlabeled data set in order to improve classification or generalization performance. Using data-dependent kernels for kernel machines one can build semi-supervised classifiers by building the kernel in such a way that feature space dot products incorporate the structure of the data set. In this paper we propose two such methods: one using specific hierarchical clustering, and another kernel for reweighting an arbitrary base kernel taking into account the cluster structure of the data." @default.
• W2341476366 created "2016-06-24" @default.
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• W2341476366 date "2010-11-01" @default.
• W2341476366 modified "2023-10-18" @default.
• W2341476366 title "Hierarchical and Reweighting Cluster Kernels for Semi-Supervised Learning" @default.
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• W2341476366 doi "https://doi.org/10.15837/ijccc.2010.4.2496" @default.
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