predict.OneNN

A model of class OneNN built with <code><a rd-options="" href="/link/oneNN?package=ssc&version=1.0" data-mini-rdoc="ssc::oneNN">oneNN</a></code>

object

A matrix of distances between the instances to classify (by rows) and
the instances used to train the model (by column)

dists

It is a string that can take two values: <code>"class"</code> for computing the class of
the instances or <code>"prob"</code> for computing the probabilities of belonging to each class.

type


This function predicts the class label of instances or its probability of
pertaining to each class based on the distance matrix.


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.OneNN function

A model of class OneNN built with <code><a rd-options='' href='oneNN'>oneNN</a></code>

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predict.OneNN: Model Predictions

Description

Usage

Arguments

Value