Popularity
77 Stars
Updated Last
1 Year Ago
Started In
February 2015

NOTE: This package has been deprecated. Please use the go-forward package located here:

https://github.com/theogf/KernelFunctions.jl

Machine Learning Kernels (DEPRECATED)

MLKernels.jl is a Julia package that provides a collection of common machine learning kernels and a set of methods to efficiently compute kernel matrices.

Package Status Build Status
License Dev Documentation Build Status Coverage Status

Documentation

Read the full documentation.

Visualization

Through the use of kernel functions, kernel-based methods may operate in a high (potentially infinite) dimensional implicit feature space without explicitly mapping data from the original feature space to the new feature space. Non-linearly separable data may be linearly separable in the transformed space. For example, the following data set is not linearly separable:

Feature Space

Using a Polynomial Kernel of degree 2, the points are mapped to a 3-dimensional space where a plane can be used to linearly separate the data:

Transformed Data

Explicitly, the Polynomial Kernel of degree 2 maps the data to a cone in 3-dimensional space. The intersecting hyperplane forms a conic section with the cone:

Transformed Data

When translated back to the original feature space, the conic section corresponds to a circle which can be used to perfectly separate the data:

Separating Hyperplane

The above plots were generated using PyPlot.jl.