The trade-off between generative and discriminative classifiers
Guillaume Bouchard and William Triggs
In: CompStat 2004, 23-27 August 2004, Prague.
Given any generative classifier based on an inexact density model, we can
define a discriminative counterpart that reduces its asymptotic error rate. We
introduce a family of classifiers that interpolate the two
approaches, thus providing a new way to compare them and giving an estimation
procedure whose classification performance is well balanced between the bias of
generative classifiers and the variance of discriminative ones. We show that an
intermediate trade-off between the two strategies is often preferable, both
theoretically and in experiments on real data.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||William Triggs|
|Deposited On:||30 December 2004|