Understanding Perceptron training algorithm
<p>The following text is from Hal Daumé III's <a href="http://ciml.info/dl/v0_9/ciml-v0_9-ch03.pdf" rel="nofollow noreferrer">"<em>A Course in Machine Learning</em>"</a> online text book (Page-41).</p>

<p><a href="https://i.stack.imgur.com/SCfew.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/SCfew.png" alt="enter image description here"></a></p>

<p>I understand that, <code>D</code> = size of the input vector.</p>

<p>(1) What kind of Perceptron algorithm is this? Binary/Multi-class? Online/Offline?</p>

<p>(2) What is <code>y</code> here? Bias/weight/Sample/class_label?</p>

<p>(3) What is the rationale of testing <code>ya&lt;=0</code> for updating weights?</p>

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<p>EDIT.</p>

<p><code>y</code> is class_label.</p>