@article {17610,
	title = {Improved Bounds on the Sample Complexity of Learning},
	journal = {Journal of Computer and System Sciences},
	volume = {62},
	year = {2001},
	month = {2001/05//},
	pages = {516 - 527},
	abstract = {We present a new general upper bound on the number of examples required to estimate all of the expectations of a set of random variables uniformly well. The quality of the estimates is measured using a variant of the relative error proposed by Haussler and Pollard. We also show that our bound is within a constant factor of the best possible. Our upper bound implies improved bounds on the sample complexity of learning according to Haussler{\textquoteright}s decision theoretic model.},
	keywords = {agnostic learning, empirical process theory, machine learning, PAC learning, sample complexity},
	isbn = {0022-0000},
	doi = {10.1006/jcss.2000.1741},
	url = {http://www.sciencedirect.com/science/article/pii/S0022000000917410},
	author = {Li,Yi and Long,Philip M. and Srinivasan, Aravind}
}
