A graphical heuristic constructs an information graph via approximate nearest neighbors and applies clustering to reduce or partition training data, achieving faster training than LIBSVM's shrinking heuristic with comparable or better prediction accuracy.
In:KDD ’00:Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, Massachusetts, USA
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A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training
A graphical heuristic constructs an information graph via approximate nearest neighbors and applies clustering to reduce or partition training data, achieving faster training than LIBSVM's shrinking heuristic with comparable or better prediction accuracy.