linfa-svm
| Crates.io | linfa-svm |
| lib.rs | linfa-svm |
| version | |
| source | src |
| created_at | 2020-11-29 16:53:38.530457+00 |
| updated_at | 2025-02-01 17:47:20.630662+00 |
| description | Support Vector Machines |
| homepage | |
| repository | https://github.com/rust-ml/linfa |
| max_upload_size | |
| id | 317828 |
| Cargo.toml error: | TOML parse error at line 18, column 1 | 18 | autolib = false | ^^^^^^^ unknown field `autolib`, expected one of `name`, `version`, `edition`, `authors`, `description`, `readme`, `license`, `repository`, `homepage`, `documentation`, `build`, `resolver`, `links`, `default-run`, `default_dash_run`, `rust-version`, `rust_dash_version`, `rust_version`, `license-file`, `license_dash_file`, `license_file`, `licenseFile`, `license_capital_file`, `forced-target`, `forced_dash_target`, `autobins`, `autotests`, `autoexamples`, `autobenches`, `publish`, `metadata`, `keywords`, `categories`, `exclude`, `include` |
| size | 0 |
wg (github:rust-ml:wg)
documentation
README
Support Vector Machines
linfa-svm provides a pure Rust implementation for support vector machines.
The Big Picture
linfa-svm is a crate in the linfa ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python's scikit-learn.
Support Vector Machines are one major branch of machine learning models and offer classification or regression analysis of labeled datasets. They seek a discriminant, which seperates the data in an optimal way, e.g. have the fewest numbers of miss-classifications and maximizes the margin between positive and negative classes. A support vector contributes to the discriminant and is therefore important for the classification/regression task. The balance between the number of support vectors and model performance can be controlled with hyperparameters.
Current State
linfa-svm currently provides an implementation of SVM with Sequential Minimal Optimization:
-
Support Vector Classification with C/Nu/one-class
-
Support Vector Regression with Epsilon/Nu
Examples
There is an usage example in the examples/ directory. To run, use:
$ cargo run --release --example winequality
License
Dual-licensed to be compatible with the Rust project.
Licensed under the Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0 or the MIT license http://opensource.org/licenses/MIT, at your option. This file may not be copied, modified, or distributed except according to those terms.