predict.EMLeastSquaresClassifierSSLR

object

<p>This parameter is included for compatibility reasons.</p>

<p>Predict EMLeastSquaresClassifierSSLR</p>

Providing a collection of techniques for semi-supervised
classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled
data are used to train a classifier. The package includes a collection of
semi-supervised learning techniques: self-training, co-training, democratic,
decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface
that is easy to use.

Francisco Jes<c3><ba>s Palomares Alabarce

SSLR

Semi-Supervised Classification, Regression and Clustering
Methods

Jos<c3><a9> Manuel Ben<c3><ad>tez

Isaac Triguero

Christoph Bergmeir

Mabel Gonz<c3><a1>lez

predict.EMLeastSquaresClassifierSSLR function

Predict EMLeastSquaresClassifierSSLR — predict.EMLeastSquaresClassifierSSLR

Predict EMLeastSquaresClassifierSSLR

predict.EMLeastSquaresClassifierSSLR: Predict EMLeastSquaresClassifierSSLR

Description

Usage

Arguments