print.dcsvm

Prints a summary of the <code>dcsvm</code> object, showing the solution paths.

Implements an efficient algorithm for solving sparse-penalized support vector machines with kernel density convolution. This package is designed for high-dimensional classification tasks, supporting lasso (L1) and elastic-net penalties for sparse feature selection and providing options for tuning kernel bandwidth and penalty weights. The 'dcsvm' is applicable to fields such as bioinformatics, image analysis, and text classification, where high-dimensional data commonly arise. Learn more about the methodology and algorithm at Wang, Zhou, Gu, and Zou (2023) <doi:10.1109/TIT.2022.3222767>.

Boxiang Wang

dcsvm

Density Convoluted Support Vector Machines

Le Zhou

Yuwen Gu

Hui Zou

print.dcsvm function

<dl><dt>x</dt>
<dd>A fitted <code>dcsvm</code> object.</dd>
<dt>digits</dt>
<dd>Specifies the significant digits to use in the output. Default is <code>max(3, getOption("digits") - 3)</code>.</dd>
<dt>...</dt>
<dd>Additional arguments to <code>print</code>.</dd></dl>

Arguments

Print a DCSVM Object — print.dcsvm

<dl>

<dt>x</dt>
<dd>A fitted <code>dcsvm</code> object.</dd>


<dt>digits</dt>
<dd>Specifies the significant digits to use in the output. Default is <code>max(3, getOption("digits") - 3)</code>.</dd>


<dt>...</dt>
<dd>Additional arguments to <code>print</code>.</dd>

</dl>

print.dcsvm: Print a DCSVM Object

Description

Usage

Value

Arguments

Details

See Also

Examples