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d5/db0/adaline__learning_8cpp.html
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<title>Algorithms_in_C++: machine_learning/adaline_learning.cpp File Reference</title>
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@@ -41,7 +41,7 @@ MathJax.Hub.Config({
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@@ -149,7 +149,9 @@ Variables</h2></td></tr>
<dl class="section author"><dt>Author</dt><dd><a href="https://github.com/kvedala" target="_blank">Krishna Vedala</a></dd></dl>
<p><a href="https://commons.wikimedia.org/wiki/File:Adaline_flow_chart.gif"><img src="https://upload.wikimedia.org/wikipedia/commons/b/be/Adaline_flow_chart.gif" alt="Structure of an ADALINE network. Source: Wikipedia" style="width:200px; float:right;" class="inline"/></a></p>
<p >ADALINE is one of the first and simplest single layer artificial neural network. The algorithm essentially implements a linear function </p><p class="formulaDsp">
\[ f\left(x_0,x_1,x_2,\ldots\right) = \sum_j x_jw_j+\theta \]
\[ f\left(x_0,x_1,x_2,\ldots\right) =
\sum_j x_jw_j+\theta
\]
</p>
<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>
</div><h2 class="groupheader">Function Documentation</h2>
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<li class="navelem"><a class="el" href="../../dir_3343723ae086de42ee4ca9774da3a13f.html">machine_learning</a></li><li class="navelem"><a class="el" href="../../d5/db0/adaline__learning_8cpp.html">adaline_learning.cpp</a></li>
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