NobleBlocks
Public

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...

Subject

Finding related papers...

Discussions

(0)

No comments yet

Be the first to share your thoughts!