Marcel R. Ackermann, Johannes Blömer, Christoph Scholz

We prove the computational hardness of three k-clustering problems using an (almost) arbitrary Bregman divergence as dissimilarity measure: (a) The Bregman k-center problem, where the objective is to find a set of centers that minimizes the maximum dissimilarity of any input point towards its closest center, and (b) the Bregman ... more >>>

Maciej Li\'skiewicz, Matthias Lutter, Rüdiger Reischuk

In certain applications there may only be positive samples available to

to learn concepts of a class of interest,

and this has to be done properly, i.e. the

hypothesis space has to coincide with the concept class,

and without false positives, i.e. the hypothesis always has be a subset ...
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