Fuzzy ARTMAP Code: Usage directions

Fuzzy ARTMAP Code Use Directions

The current folder contains the code and data required to run all the examples in the description below.

To execute examples, select Run tab and select one of the following datasets from the dropdown menu

1. 1: Circle in Square benchmark (sparse)
2. 2: Circle in Square benchmark (dense)
3. 3: Stripes benchmark (sparse)
4. 4: Stripes benchmark (dense)
5. 5: Checkerboard benchmark (sparse)
6. 6: Checkerboard benchmark (dense)
7. 7: Boston Benchmark: test on stripe 1
8. 8: Boston Benchmark: test on stripe 2
9. 9: Boston Benchmark: test on stripe 3
10. 10: Boston Benchmark: test on stripe 4
11. 11: Movie Genre Benchmark

To provide your own dataset, run fuzzyARTMAPTester

Usage: [a,b,c] = fuzzyARTMAPTester(dataStruct)

The MATLAB struct dataStruct should have the following format.

The datastruct fields are:

training_input: [f features X m records]
training_output: [m labels X 1]
test_input: [f features X n records]
test_output: [n labels X 1]
description: 'dataset_title'
descriptionVerbose: 'A more verbose description of the dataset'

Reference:

Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J.H., & Rosen, D.B. (1992) Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks, 3, 698-713.

Disclaimer:

This software is provided free of charge. As such, the authors assume no responsibility for the programs' behavior. While they have been tested and used in-house for three years, no claim is made that Fuzzy ARTMAP implementations are correct or bug-free. They are used and provided solely for research and educational purposes. No liability, financial or otherwise, is assumed regarding any application of Fuzzy ARTMAP.