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Learning Reductions That Really Work
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AI
DOI：10.1109/JPROC.2015.2494118.png)
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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
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