Classifier selection using the predicate depth
Published in Journal of Machine Learning Research • Jan 1, 2013
NobleIDNI2P19W28R48S24
Authors:,
Ran Gilad-Bachrach
Christopher J. C. Burges
Abstract
Typically, one approaches a supervised machine learning problem by writing down an objective function and finding a hypothesis that minimizes it. This is equivalent to finding the Maximum A Posteriori (MAP) hypothesis for a Boltzmann distribution. However, MAP is not a robust statistic. We present a...
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