Abstract
We study learning of probability distributions characterized by an unknown symmetry direction. Based on an entropic performance measure and the variational method of statistical mechanics we develop exact upper and lower bounds on the scaled critical number of examples below which learning of the direction is impossible. The asymptotic tightness of the bounds suggests an asymptotically optimal method for learning nonsmooth distributions.
- Received 19 September 2000
DOI:https://doi.org/10.1103/PhysRevLett.86.2174
©2001 American Physical Society