predict.transDat

Given the input parameters, which are generated from
<code>transformdata</code>, it performs the same transformation
with the same settings to the given input

Implements the SVM-Maj algorithm to train data with support vector machine
<doi:10.1007/s11634-008-0020-9>.
This algorithm uses two efficient updates, one for linear kernel and one
for the nonlinear kernel.

Hok San Yip

SVMMaj

Implementation of the SVM-Maj Algorithm

Hoksan Yip

Georgi Nalbantov

predict.transDat function

<dl><dt>x</dt>
<dd>a (new) vector of numerics to be transformed</dd>
<dt>attrib</dt>
<dd>either a list of settings, or <code>NULL</code> in case
the attributes are given as separate input</dd>
<dt>values</dt>
<dd>a vector of levels in case <code>x</code> is a factor</dd>
<dt>standardization</dt>
<dd>the standardization rules from <code>normalize</code></dd>
<dt>splineInterval</dt>
<dd>the knots to be used for spline basis</dd>
<dt>splineDegree</dt>
<dd>the polynomial degree of the splines</dd></dl>

Arguments

Perform the transformation based on predefined settings — predict.transDat

<dl>

<dt>x</dt>
<dd>a (new) vector of numerics to be transformed</dd>


<dt>attrib</dt>
<dd>either a list of settings, or <code>NULL</code> in case
the attributes are given as separate input</dd>


<dt>values</dt>
<dd>a vector of levels in case <code>x</code> is a factor</dd>


<dt>standardization</dt>
<dd>the standardization rules from <code>normalize</code></dd>


<dt>splineInterval</dt>
<dd>the knots to be used for spline basis</dd>


<dt>splineDegree</dt>
<dd>the polynomial degree of the splines</dd>

</dl>

predict.transDat: Perform the transformation based on predefined settings

Description

Usage

Value

Arguments