Free software to download (for research purposes)
and material accompanying my latest book:
The software is provided under the GNU license for Research purposes only (for commercial use - please, contact Prof. Angelov, Lancaster University; the open source code does NOT provide rights to use the software (and patented IP methods for any commercial purposes without a license agreement with Lancaster University). In any publication when using the software and the methods, please, make appropriate references to avoid plagiarism claims.
A. Code @ GitHub for:
A1. IDEAL (Neurocomputing paper D2)
A2. xDNN (Neural Networks paper D35)
A3. Deep Fake Detection method (ICCV and CVPR papers E13 and E9, respectively)
A4. ViT unsueprvised domain adaptation (ICLR paper E10)
A5. xDNN application to Covid CT scan data (Evolving Systems paper D9, TexRxiv paper F12 and medRxiv paper F14)
B. Accompanying material to my latest book
"Empirical Approach to Machine Learning"
Springer, 2018, ISBN 9783030023836; DOI 10.1007/978-3-030-02384-3
(for the URL - please, press the book image)
B1. Lecture Notes and Lab session notes forming a course
B2. The software accompanying the book can be downloaded from the
(see also the hyperlinks below to specific algorithms/functions)
C. ALMMo (Autonomous Learning Multi-Model) Systems family:
-
DRB Classifier
-
Semi-supervised DRB (SS-DRB) Classifier
-
Autonomous Data Partitioning (ADP) Algorithm
-
Self-Organising Fuzzy Logic Classifier
-
SODA (Self-organising Direction-aware) Partitioning
D. Empirical Fuzzy Sets (EFS) software
E. Autonomous Anomaly Detection Algorithm
F. Autonomous Data-Density-based (ADD) Clustering
G. Autonomous Data-Driven Clustering for Streaming Data
H. TEDA Clustering - parallel version
I. EST Matlab package can be received upon request and includes:
-
eTS+ (2001-2010)
-
eClass (2006-2008)
-
eClustering
-
RDE (2001-2010)
