Learning Reductions That Really Work

doi:10.1109/JPROC.2015.2494118

Learning Reductions That Really Work

2016-01-01

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DOI：10.1109/JPROC.2015.2494118

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Abstract

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In this paper, we provide a summary of the mathematical and computational techniques that have enabled learning reductions to effectively address a wide class of tasks, and show that this approach to solving machine learning problems can be broadly useful. Our work is instantiated and tested in a machine learning library, Vowpal Wabbit, to prove that the techniques discussed here are fully viable in practice.

Keywords:

Learning systems

machine learning

prediction methods

Learning Reductions That Really Work

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