Please use this identifier to cite or link to this item:
Title: ANN’s vs. SVM’s for image classification
Authors: Moorgas, Kevin Emanuel 
Pillay, Nelendran 
Governder, Poobalan 
Keywords: Hyperplane;Support vector machine;Artificial neural network;Principle component analysis
Issue Date: Aug-2012
Publisher: International ASET
Source: Govender, P., Pillay, N., Moorgas, K.E. 2012. ANN's vs. SVM's for Image Classification. International Conference on Electrical and Computer Systems Ottawa: Internationa ASET.
In this paper the dynamic performance of the artificial neural network is compared to the performance of a statistical method such as the support vector machine. This comparison is made with respect to an image classification application where the performance is compared with regards to generalization and robustness. Image vectors are compressed in order to reduce the dimensionality and the salient feature vectors are extracted with the principle component algorithm. The artificial neural network and the support vector machine are trained to classify images with feature vectors. A comparative analysis is made between the artificial neural network and the support vector machine with respect to robustness and generalization.
Appears in Collections:Research Publications (Engineering and Built Environment)

Files in This Item:
File Description SizeFormat
poobie_2012_proceeding.pdf451.5 kBAdobe PDFThumbnail
Show full item record

Page view(s) 50

checked on Jul 13, 2024

Download(s) 50

checked on Jul 13, 2024

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.