PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Quantization for nonparametric regression
Laszlo Gyorfi and Marten Wegkamp
IEEE Transactions on Information Theory Volume 54, Number 2, pp. 867-874, 2008. ISSN 0018-9448


The authors discuss quantization or clustering of non-parametric regression estimates. The main tools developed are oracle inequalities for the rate of convergence of constrained least squares estimates. These inequalities yield fast rates for both nonparametric (unconstrained) least squares regression and clustering of partition regression estimates and plug-in empirical quantizers. The bounds on the rate of convergence generalize known results for bounded errors to subGaussian, too.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4995
Deposited By:Laszlo Gyorfi
Deposited On:18 March 2009