transformdata

Performs subsequently a normalization of the input data and
creating spline basis based on the user defined 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

transformdata function

<dl><dt>x</dt>
<dd>a single column of values as input for the data
transformation</dd>
<dt>standardize</dt>
<dd>Either a string value denoting a predefined scaling, or a
list with values <code>a</code> and <code>b</code> corresponding with the numeric
centering and scaling, that is, using the function
<code>x * standardize$b - standardize$a</code>.</dd>
<dt>spline.knots</dt>
<dd>Number of inner knots to use. <code>isb</code> will equally
distribute the knots over the value range using quantiles.
<code>spline.knots</code> will only be used if <code>knots</code> is not given.</dd>
<dt>spline.degree</dt>
<dd>The polynomial degree of the spline basis.</dd></dl>

Arguments

Transform the data with normalization and/or spline basis — transformdata

<dl>

<dt>x</dt>
<dd>a single column of values as input for the data
transformation</dd>


<dt>standardize</dt>
<dd>Either a string value denoting a predefined scaling, or a
list with values <code>a</code> and <code>b</code> corresponding with the numeric
centering and scaling, that is, using the function
<code>x * standardize$b - standardize$a</code>.</dd>


<dt>spline.knots</dt>
<dd>Number of inner knots to use. <code>isb</code> will equally
distribute the knots over the value range using quantiles.
<code>spline.knots</code> will only be used if <code>knots</code> is not given.</dd>


<dt>spline.degree</dt>
<dd>The polynomial degree of the spline basis.</dd>

</dl>

transformdata: Transform the data with normalization and/or spline basis

Description

Usage

Value

Arguments