TITLE:
Error Analysis of ERM Algorithm with Unbounded and Non-Identical Sampling
AUTHORS:
Weilin Nie, Cheng Wang
KEYWORDS:
Learning Theory, ERM, Non-Identical, Unbounded Sampling, Covering Number
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.4 No.1,
January
27,
2016
ABSTRACT: A standard assumption in the literature of learning theory is the samples which are drawn independently from an identical distribution with a uniform bounded output. This excludes the common case with Gaussian distribution. In this paper we extend these assumptions to a general case. To be precise, samples are drawn from a sequence of unbounded and non-identical probability distributions. By drift error analysis and Bennett inequality for the unbounded random variables, we derive a satisfactory learning rate for the ERM algorithm.