triTrainingCombine

<p>A list with the predictions of each classifiers</p>

pred

<p>This function combines the predictions obtained 
by the set of classifiers.</p>

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
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 supervised base classifier. At low ratios of labeled data, it
can be shown to perform better than classical supervised classifiers.

Christoph Bergmeir

Semi-Supervised Classification Methods

Mabel Gonz<c3><a1>lez

Osmani Rosado-Falc<c3><b3>n

Jos<c3><a9> Daniel Rodr<c3><ad>guez

Isaac Triguero

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

triTrainingCombine function

Combining the hypothesis — triTrainingCombine

Combining the hypothesis

triTrainingCombine: Combining the hypothesis

Description

Usage

Arguments

Value