Sample dispersion is better than sample discrepancy for classification
Benoît Gandar, Gaëlle Loosli and Guillaume Deffuant
We want to generate learning data within the context of active learning. First, we recall theoretical results proposing discrepancy as a criterion for generating sample in regression. We show surprisingly that theoretical results about low discrepancy sequences in regression problems are not adequate for classification problems. Secondly we propose dispersion as a criterion for generating data. Then, we present numerical experiments which have a good degree of adequacy with theory.
|EPrint Type:||Monograph (Technical Report)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Gaëlle Loosli|
|Deposited On:||08 March 2011|