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OVERVIEW

The focus of the neural networks group is to investigate aspects of training and optimization of neural networks, and to apply neural networks to solve real-world problems. The activities of this focus area are mainly centered around architecture selection, active learning, and the development of new an efficient training algorithms. Some work is done on self-organizing maps.

Current applications are directed towards data mining, spam detection, user authentication, fraud detection, gesture recognition, and trading on financial markets. Applications of self-organization maps to exploratory data analysis, data mining, and species identification are done.

ACTIVE MEMBERS

List the current members actively doing research in this focus area. [ Show ]

ALUMNI MEMBERS

S van der Stockt

M.Sc Completed in 2008

E Dean

M.Sc Started in 2002

J Pun

M.Sc Completed

E Clements

Hons-B.Sc Completed in 2003

U Paquet

M.Sc Completed in 2003

R van den Hoven

Hons-B.Sc Completed in 2003

A Ismail

PhD Started in 2005
M.Sc Completed in 2001

A Adejumo

M.Sc Completed in 1999

GROUP PUBLICATIONS

List publications of this research focus area. [ Show ]

MEMBER PROFILE



 Name:

 Eileen Dean

Portrait photo

 E-mail:

 deane@dit.ac.za

 Group(s):

 Data Mining

 

 Degree specific information: M.Sc

 Title:

 Artifical Intelligence Approach to Multi-Attribute Pattern Recognition for Tree Identification

 Abstract:

The aim of this thesis is to investigate Self-Organizing Maps (SOM) with a view to overcoming the problems of identification of biological tree specimens. The problems of identification of any biological material arise because of the variability exhibited by the phenotypes of any living organism. The knowledge obtained from domain experts on identified trees will be used to train a SOM neural network to discern amongst the different tree species (accomplished by performing a clustering process which groups together trees with similar characteristics). Given the available information on a speciment to be identified (often consisting of sparse data), the SOM will match the cluster formed from the unknown specimen with the closest cluster of the known tree samples and assign a probability. The success of the SOM neural Network to identify selected specimen will be tested and analysed.

 Supervisor / Co-Supervisor:

 AP Engelbrecht

 Thesis:

 Not available for download yet.




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