AdaptiveResonance.jl

These pages serve as the official documentation for the AdaptiveResonance.jl Julia package.

Adaptive Resonance Theory (ART) began as a neurocognitive theory of how fields of cells can continuously learn stable representations, and it evolved into the basis for a myriad of practical machine learning algorithms. Pioneered by Stephen Grossberg and Gail Carpenter, the field has had contributions across many years and from many disciplines, resulting in a plethora of engineering applications and theoretical advancements that have enabled ART-based algorithms to compete with many other modern learning and clustering algorithms.

The purpose of this package is to provide a home for the development and use of these ART-based machine learning algorithms.

See the Index for the complete list of documented functions and types.

Manual Outline

This documentation is split into the following sections:

• Package Guide
• Examples
• Contributing
• Index

The Package Guide provides a tutorial to the full usage of the package, while Examples gives sample workflows using a variety of ART modules.

Instructions on how to contribute to the package are found in Contributing, and docstrings for every element of the package is listed in the Index.