predict.setred

SETRED model object built with <code><a rd-options="" href="/link/setred?package=ssc&version=1.0" data-mini-rdoc="ssc::setred">setred</a></code> function.

object

A object that can be coerced as matrix.
Depending on how was the model built, <code>x</code> is interpreted as a matrix
with the distances between the unseen instances and the selected training instances,
or a matrix of instances.

Additional objects that can be coerced as matrix.
Depending on how was the model built, this objects are used or ignored.
This objects are interpreted as matrices of distances between the unseen instances
and the selected training instances.


Predicts the label of instances according to SETRED model.


Provides a collection of self-labeled techniques for semi-supervised classification. In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a distance-based classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a suitable base classifier and distance measure. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.

Christoph Bergmeir

Semi-Supervised Classification Methods

predict.setred function

SETRED model object built with <code><a rd-options='' href='setred'>setred</a></code> function.

predict.setred: Model Predictions

Description

Usage

Arguments

Value

Details