Empirical Approach to Machine Learning Software Package

Versione 1.0.0 (821 KB) da X.Gu&P.Angelov
This package contains the supplementary software for the book titled: Empirical Approach to Machine Learning.
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This package contains the supplementary software for the book titled: Empirical Approach to Machine Learning.

This package is composed of:
1. AAD.m - Autonomous Anomaly Detection Algorithm
2. ADP.m - Autonomous Data Partitioning Algorithm
3. ALMMo0.m - Autonomous Learning Multi-Model System of Zero-Order
4. ALMMo1.m - Autonomous Learning Multi-Model System of First-Order
5. DRB.m - Deep Rule-Based System
6. SSDRB.m - Semi-Supervised Deep Rule-Based System
7. ASSDRB.m - Active Semi-Supervised Deep Rule-Based System
and a few datasets for demonstration.

The detailed instructions for the source codes can be found in:

P. Angelov, X. Gu, "Empirical Approach to Machine Learning," Springer, ISBN: 978-3-030-02383-6, 2018.

Please cite this software package using the above reference if it helps.

For any queries about the codes, please contact Prof. Plamen P. Angelov (p.angelov@lancaster.ac.uk) and Dr. Xiaowei Gu (x.gu3@lancaster.ac.uk)

Programmed by Xiaowei Gu

Cita come

X.Gu&P.Angelov (2024). Empirical Approach to Machine Learning Software Package (https://www.mathworks.com/matlabcentral/fileexchange/69012-empirical-approach-to-machine-learning-software-package), MATLAB Central File Exchange. Recuperato .

Compatibilità della release di MATLAB
Creato con R2018b
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SupplementarySourceCodes

Versione Pubblicato Note della release
1.0.0