7 releases (4 breaking)
|0.6.0||Jun 15, 2022|
|0.5.1||Mar 1, 2022|
|0.5.0||Oct 21, 2021|
|0.4.0||Apr 28, 2021|
|0.2.1||Nov 29, 2020|
#222 in Machine learning
432 downloads per month
Used in 4 crates (3 directly)
linfa-kernel provides methods for dimensionality expansion.
The Big Picture
linfa-kernel 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
In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine. They owe their name to the kernel functions, which maps the features to some higher-dimensional target space. Common examples for kernel functions are the radial basis function (euclidean distance) or polynomial kernels.
linfa-kernel currently provides an implementation of kernel methods for RBF and polynomial kernels, with sparse or dense representation. Further a k-neighbour approximation allows to reduce the kernel matrix size.
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.