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<div class="headertitle"><div class="title">adaline_learning.cpp File Reference<div class="ingroups"><a class="el" href="../../d9/d66/group__machine__learning.html">Machine Learning Algorithms</a></div></div></div>
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<p><a href="../../d5/db0/adaline__learning_8cpp_source.html">Go to the source code of this file.</a></p>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
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<div class="textblock"><p><a href="https://en.wikipedia.org/wiki/ADALINE" target="_blank">Adaptive Linear Neuron (ADALINE)</a> implementation </p>
<dl class="section author"><dt>Author</dt><dd><a href="https://github.com/kvedala" target="_blank">Krishna Vedala</a></dd></dl>
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<p> where \(x_j\) are the input features of a sample, \(w_j\) are the coefficients of the linear function and \(\theta\) is a constant. If we know the \(w_j\), then for any given set of features, \(y\) can be computed. Computing the \(w_j\) is a supervised learning algorithm wherein a set of features and their corresponding outputs are given and weights are computed using stochastic gradient descent method. </p>
<p class="definition">Definition in file <a class="el" href="../../d5/db0/adaline__learning_8cpp_source.html">adaline_learning.cpp</a>.</p>
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