matLearn: machine learning algorithm implementations in Matlab

The matLearn package contains Matlab implementations of a wide variety of the most commonly-used machine learning algorithms, all using a simple common interface. It in particular focuses on the following tasks:


Documentation

The documentation for matLearn consists of a set of examples that demonstrate the basic usage of matLearn methods on a variety of synthetic and real datasets. These examples are described below. All matLearn algorithms follow a common method interface:

Supervised Interface: Unsupervised Interface:

Looking for an example of a particular machine learning model by name? Check out the "List of files" under each machine learning category below.

Data Description

In the task examples, a number of synthetic and real datasets are used to exhibit strengths and weaknesses of the algorithms. The datasets are described here.

Examples by Task

Regression

List of files

Examples:

Binary Classification

List of files

Additional Input Specifications:

Examples:

Multi-class Classification

List of files

Additional Input Specifications:

Discriminative Models

  • y: elements must be integer values representing classes
Generative Models
  • y: elements are disregarded

Examples:

Multilabel Classification

List of files

Examples:

Ordinal Data Classification

List of files

Examples:

Unsupervised Learning

List of files

Examples:

Download

Download link for complete set of files: matLearn2016.zip

Citations

If you use this software in a publication, please cite the version of the work based on the year the package was downloaded using the following information:
Mark Schmidt > Software > minFunc