dcsvm-package

This package provides tools to perform density-convoluted support vector machine (DCSVM) modeling for high-dimensional data classification.

classification

High-dimensional

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

dcsvm-package function

Boxiang Wang, Le Zhou, Yuwen Gu, and Hui Zou Maintainer:
Boxiang Wang &lt;boxiang-wang@uiowa.edu&gt;

Author

Density-Convoluted Support Vector Machines — dcsvm-package

Boxiang Wang, Le Zhou, Yuwen Gu, and Hui Zou Maintainer:
Boxiang Wang &lt;boxiang-wang@uiowa.edu&gt;

Package:	dcsvm
Type:	Package
Version:	0.0.1
Date:	2025-01-08
License:	GPL-2

dcsvm-package: Density-Convoluted Support Vector Machines

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References