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- W2221195077 abstract "We compose a toolbox for the design of Minimum Disagreement algorithms. This box contains general procedures which transform (without much loss of efficiency) algorithms that are successful for some d-dimensional (geometric) concept class $$mathcal{C}$$ into algorithms which are successful for a $$(d+1)$$ -dimensional extension of $$mathcal{C}$$ . An iterative application of these transformations has the potential of starting with a base algorithm for a trivial problem and ending up at a smart algorithm for a non-trivial problem. In order to make this working, it is essential that the algorithms are not proper, i.e., they return a hypothesis that is not necessarily a member of $$mathcal{C}$$ . However, the “price” for using a super-class $$mathcal{H}$$ of $$mathcal{C}$$ is so low that the resulting time bound for achieving accuracy $$varepsilon $$ in the model of agnostic learning is significantly smaller than the time bounds achieved by the up to date best (proper) algorithms. We evaluate the transformation technique for $$d=2$$ on both artificial and real-life data sets and demonstrate that it provides a fast algorithm, which can successfully solve practical problems on large data sets." @default.
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- W2221195077 date "2015-01-01" @default.
- W2221195077 modified "2023-10-14" @default.
- W2221195077 title "Hierarchical Design of Fast Minimum Disagreement Algorithms" @default.
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- W2221195077 doi "https://doi.org/10.1007/978-3-319-24486-0_9" @default.
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