Compressed Learning with Regular Concept

摘要

We revisit compressed learning in the PAC learning framework. Specifically, we derive error bounds for learning half-space concepts with compressed data. We propose the regularity assumption over a pair of concept and data distribution to greatly generalize former assumptions. For a regular concept we define a robust factor to characterize the margin distribution and show that such a factor tightly controls the generalization error of a learned classifier. Moreover, we extend our analysis to the more general linearly non-separable case. Empirical results on both toy and real world data validate our analysis.