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The current trend of growth of information reveals that it is inevitable that large-scale learning problems become the norm. In this paper, we propose and analyze a novel Low-density Cut based tree Decomposition method for large-scale SVM problems, called LCD-SVM. The basic idea here is divide and conquer: use a decision tree to decompose the data space and train SVMs on the decomposed regions. Specifically,...
In this paper, we apply the clustering feature tree to large scale graph-based semi-supervised problems and propose a local learning integrating global structure algorithm. By organizing the unlabeled samples with a clustering feature tree, it allows us to decompose the unlabeled samples to a series of clusters (sub-trees) and learn them locally. In each training process on sub-trees, the clustering...
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