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<table width="100%" summary="page for lvq2 {class}"><tr><td>lvq2 {class}</td><td align="right">R Documentation</td></tr></table>
<h2>Learning Vector Quantization 2.1</h2>
<h3>Description</h3>
<p>
Moves examples in a codebook to better represent the training set.
</p>
<h3>Usage</h3>
<pre>
lvq2(x, cl, codebk, niter = 100 * nrow(codebk$x), alpha = 0.03,
win = 0.3)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>x</code></td>
<td>
a matrix or data frame of examples
</td></tr>
<tr valign="top"><td><code>cl</code></td>
<td>
a vector or factor of classifications for the examples
</td></tr>
<tr valign="top"><td><code>codebk</code></td>
<td>
a codebook
</td></tr>
<tr valign="top"><td><code>niter</code></td>
<td>
number of iterations
</td></tr>
<tr valign="top"><td><code>alpha</code></td>
<td>
constant for training
</td></tr>
<tr valign="top"><td><code>win</code></td>
<td>
a tolerance for the closeness of the two nearest vectors.
</td></tr>
</table>
<h3>Details</h3>
<p>
Selects <code>niter</code> examples at random with replacement, and adjusts the nearest
two examples in the codebook if one is correct and the other incorrect.
</p>
<h3>Value</h3>
<p>
A codebook, represented as a list with components <code>x</code> and <code>cl</code>
giving the examples and classes.</p>
<h3>References</h3>
<p>
Kohonen, T. (1990) The self-organizing map.
<EM>Proc. IEEE</EM>
<B>78</B>, 1464–1480.
</p>
<p>
Kohonen, T. (1995)
<EM>Self-Organizing Maps.</EM>
Springer, Berlin.
</p>
<p>
Ripley, B. D. (1996)
<EM>Pattern Recognition and Neural Networks.</EM> Cambridge.
</p>
<p>
Venables, W. N. and Ripley, B. D. (2002)
<EM>Modern Applied Statistics with S.</EM> Fourth edition. Springer.
</p>
<h3>See Also</h3>
<p>
<code><a href="lvqinit.html">lvqinit</a></code>, <code><a href="lvq1.html">lvq1</a></code>, <code><a href="olvq1.html">olvq1</a></code>,
<code><a href="lvq3.html">lvq3</a></code>, <code><a href="lvqtest.html">lvqtest</a></code>
</p>
<h3>Examples</h3>
<pre>
data(iris3)
train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
cd <- lvqinit(train, cl, 10)
lvqtest(cd, train)
cd0 <- olvq1(train, cl, cd)
lvqtest(cd0, train)
cd2 <- lvq2(train, cl, cd0)
lvqtest(cd2, train)
</pre>
<hr><div align="center">[Package <em>class</em> version 7.2-44 <a href="00Index.html">Index]</a></div>
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