wlda_error: Measures the expected error of the LDA model defined by m, p,...

In RSSL: Implementations of Semi-Supervised Learning Approaches for Classification

Measures the expected error of the LDA model defined by m, p, and iW on the data set a, where weights w are potentially taken into account

Description

Measures the expected error of the LDA model defined by m, p, and iW on the data set a, where weights w are potentially taken into account

Usage

wlda_error(m, p, iW, a, w)

Arguments

m	means
p	class prior
iW	is the inverse of the within covariance matrix
a	design matrix
w	weights

RSSL documentation built on May 29, 2024, 2:38 a.m.