An inequality for nearly log-concave distributions with applications to learning
We prove that given a nearly log-concave distribution, in any partition of the space to two well separated sets, the measure of the points that do not belong to these sets is large. We apply this isoperimetric inequality to derive lower bounds on the generalization error in learning. We further consider regression problems and show that if the inputs and outputs are sampled from a nearly log-concave distribution, the measure of points for which the prediction is wrong by more than epsilon(0) and less than epsilon(1) is (roughly) linear in epsilon(1) - epsilon(0), as long as epsilon(0) is not too small, and epsilon(1) not too large. We also show that when the data are sampled from a nearly log-concave distribution, the margin cannot be large in a strong probabilistic sense.
generalization error margin classification statistical learning theory