classify

Function classifying an observation.

Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) <doi:10.1109/ICPR.1992.201708>, including popular variants like extreme learning machines, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, sparse RWNN, Zhang et al. (2019) <doi:10.1016/j.neunet.2019.01.007>, and deep RWNN, Henríquez et al. (2018) <doi:10.1109/IJCNN.2018.8489703>. It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) <doi:10.1109/ECCE47101.2021.9595113>, boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) <doi:10.1016/j.patcog.2021.107978>.

Sc3<b8>ren Vilsen

RWNN

Random Weight Neural Networks

classify function

<dl><dt>y</dt>
<dd>A matrix of predicted classes.</dd>
<dt>C</dt>
<dd>A vector of class names corresponding to the columns of <code>y</code>.</dd>
<dt>t</dt>
<dd>The decision threshold which the predictions have to exceed (defaults to '0').</dd>
<dt>b</dt>
<dd>A buffer which the largest prediction has to exceed when compared to the second largest prediction (defaults to '0').</dd></dl>

Arguments

Classifier — classify

<dl>

<dt>y</dt>
<dd>A matrix of predicted classes.</dd>


<dt>C</dt>
<dd>A vector of class names corresponding to the columns of <code>y</code>.</dd>


<dt>t</dt>
<dd>The decision threshold which the predictions have to exceed (defaults to '0').</dd>


<dt>b</dt>
<dd>A buffer which the largest prediction has to exceed when compared to the second largest prediction (defaults to '0').</dd>

</dl>

classify: Classifier

Description

Usage

Value

Arguments