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Electronic Colloquium on Computational Complexity

Under the auspices of the Computational Complexity Foundation (CCF)

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Reports tagged with algorithmic learning theory:
TR11-015 | 8th December 2010
Marcel R. Ackermann, Johannes Blömer, Christoph Scholz

Hardness and Non-Approximability of Bregman Clustering Problems

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 >>>

TR17-114 | 1st July 2017
Maciej Li\'skiewicz, Matthias Lutter, Rüdiger Reischuk

Proper Learning of k-term DNF Formulas from Satisfying Assignments

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 ... more >>>

ISSN 1433-8092 | Imprint