oneNN

This argument is not used, the reason why he gets is to fulfill an agreement

a vector with the labels of training instances

Build a model using the given data to be able
to predict the label or the probabilities of other instances,
according to 1-NN algorithm.

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

oneNN: 1-NN supervised classifier builder

Description

Usage

Arguments

Value