wine

<p>This dataset is the result of a chemical analysis of wine grown in the same
region in Italy but derived from three different cultivars. The analysis determined
the quantities of 13 constituents found in each of the three types of wines.</p>

datasets

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

wine function

<p>A data frame with 178 rows and 14 variables including the class.</p>

Format

Wine recognition data — wine

Wine recognition data

wine: Wine recognition data

Description

Usage

Arguments

Format

Details