predict.selfTraining

Self-training model built with the <code><a rd-options="" href="/link/selfTraining?package=SSLR&version=0.9.3.3" data-mini-rdoc="SSLR::selfTraining">selfTraining</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.

type

This parameter is included for compatibility reasons.

Predicts the label of instances according to the <code>selfTraining</code> model.

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.selfTraining function

Self-training model built with the <code><a rd-options='' href='selfTraining'>selfTraining</a></code> function.

predict.selfTraining: Predictions of the Self-training method

Description

Usage

Arguments

Value

Details