pnnCMA-methods

pnnCMA,matrix,numeric,missing-method

pnnCMA,matrix,factor,missing-method

pnnCMA,data.frame,missing,formula-method

pnnCMA,ExpressionSet,character,missing-method


             Probabilistic Neural Networks is the term Specht (1990) used
             for a Gaussian kernel estimator for the conditional class
             densities.


multivariate

This package provides a comprehensive collection of various microarray-based classification algorithms
both from Machine Learning and Statistics.
Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed
combined or stepwise in a user-friendly environment.

Christoph Bernau

Synthesis of microarray-based classification

pnnCMA-methods function

<dl> <dt>X = "matrix", y = "numeric", f = "missing"</dt><dd>signature 1</dd> <dt>X = "matrix", y = "factor", f = "missing"</dt><dd>signature 2</dd> <dt>X = "data.frame", y = "missing", f = "formula"</dt><dd>signature 3</dd> <dt>X = "ExpressionSet", y = "character", f = "missing"</dt><dd>signature 4</dd> </dl> For references, further argument and output information, consult
<code><a rd-options="" href="/link/pnnCMA?package=CMA&version=1.30.0" data-mini-rdoc="CMA::pnnCMA">pnnCMA</a></code>.

Methods

Probabilistic Neural Networks is the term Specht (1990) used
             for a Gaussian kernel estimator for the conditional class
             densities.

pnnCMA-methods: Probabilistic Neural Networks

Description

Arguments

Methods