Relation to "many vcs buffers"
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I think this method has some relation with the popular many VCS buffers way, in which each VCS buffer forms a probability space, and by accumulating, the best group of probability space is chosen, and when having less data, it downgrades automatically.
k-nn with many dimensions does similar thing, only smoother imo.