wlda_loglik

<p>is the inverse of the within covariance matrix</p>

<p>Measures the expected log-likelihood of the LDA model defined by m, p,
and iW on the data set a, where weights w are potentially taken into
account</p>

A collection of implementations of semi-supervised classifiers
and methods to evaluate their performance. The package includes implementations
of, among others, Implicitly Constrained Learning, Moment Constrained Learning,
the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic
Likelihood estimation, S4VM and WellSVM.

Jesse Krijthe

RSSL

Implementations of Semi-Supervised Learning Approaches for
Classification

wlda_loglik function

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

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

Description

Usage

Arguments

Value