Parallelizing Spectral Algorithms for Kernel Learning

24 Oct 2016 Gilles Blanchard Nicole Mücke

We consider a distributed learning approach in supervised learning for a large class of spectral regularization methods in an RKHS framework. The data set of size n is partitioned into $m=O(n^\alpha)$ disjoint subsets... (read more)

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