Density Convoluted Support Vector Machines
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- WARNING12 OK, 1 WARNING2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 12 (1 internal)includes a package overview page
- Examples that run
- 100%10 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 62%
- Return-value docs
- 100%
- Help pages citing references
- 17%
Downloads
Dependencies
Installing dcsvm adds no packages beyond those that come with R.
Code & Tests
Datasets
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 0.0.1Latest2025-01-10 · current release
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2025-01-10
- Total releases
- 1 / 1 yrs
- License
- GPL-2 OSI
- Bundled data
- 920 KB / 1 file
- Download size
- 958 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("dcsvm")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1109/tit.2022.3222767.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.