analyzeClassification

method

lower bound, e.g. in 402040: l=0.4

upper bound, e.g. in 402040: h=0.6

This function converts the continuous outputs to binary outputs that can be
used for classification. The two methods 402040, and winner-takes-all (WTA),
are implemented as described in the SNNS User Manual 4.2.

The Stuttgart Neural Network Simulator (SNNS) is a library
containing many standard implementations of neural networks. This
package wraps the SNNS functionality to make it available from
within R. Using the 'RSNNS' low-level interface, all of the
algorithmic functionality and flexibility of SNNS can be accessed.
Furthermore, the package contains a convenient high-level
interface, so that the most common neural network topologies and
learning algorithms integrate seamlessly into R.

Christoph Bergmeir

RSNNS

Neural Networks using the Stuttgart Neural Network Simulator
(SNNS)

José M. Benítez

Andreas Zell

Niels Mache

Günter Mamier

Michael Vogt

Sven Döring

Ralf Hübner

Kai-Uwe Herrmann

Tobias Soyez

Michael Schmalzl

Tilman Sommer

Artemis Hatzigeorgiou

Dietmar Posselt

Tobias Schreiner

Bernward Kett

Martin Reczko

Martin Riedmiller

Mark Seemann

Marcus Ritt

Jamie DeCoster

Jochen Biedermann

Joachim Danz

Christian Wehrfritz

Patrick Kursawe

Andre El-Ama

analyzeClassification function

<dl><dt>y</dt>
<dd>inputs</dd>
<dt>method</dt>
<dd>"WTA" or "402040"</dd>
<dt>l</dt>
<dd>lower bound, e.g. in 402040: l=0.4</dd>
<dt>h</dt>
<dd>upper bound, e.g. in 402040: h=0.6</dd></dl>

Arguments

Converts continuous outputs to class labels — analyzeClassification

<dl>

<dt>y</dt>
<dd>inputs</dd>


<dt>method</dt>
<dd>"WTA" or "402040"</dd>


<dt>l</dt>
<dd>lower bound, e.g. in 402040: l=0.4</dd>


<dt>h</dt>
<dd>upper bound, e.g. in 402040: h=0.6</dd>

</dl>

Converts continuous outputs to class labels

analyzeClassification: Converts continuous outputs to class labels

Description

Usage

Value

Arguments

Details

References

See Also