DocumentCode
461681
Title
Back-Propagation and K-Means Algorithms Comparison
Author
Skorpil, Vladislav ; Stastny, Jiri
Author_Institution
Dept. of Telecommun., Brno Univ. of Technol.
Volume
3
fYear
2006
fDate
16-20 2006
Abstract
The paper describes the application of algorithms for object classification by using artificial neural networks. The MLP (multi layer perceptron) and RBF (radial basis function) neural networks were used. We compared results obtained by a using of learning algorithms back-propagation (BP) and K-means. The real technological scene for object classification was simulated with digitization of two-dimensional pictures. The principles and algorithms given below have been used in an application that was developed at Brno University of Technology
Keywords
backpropagation; image classification; multilayer perceptrons; radial basis function networks; K-means algorithms comparison; MLP; RBF; artificial neural networks; back-propagation; multi layer perceptron; object classification; radial basis function; two-dimensional pictures digitization; Artificial neural networks; Automation; Backpropagation algorithms; Computer networks; Computer science; Computer vision; Iterative algorithms; Neural networks; Neurons; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing, 2006 8th International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-9736-3
Electronic_ISBN
0-7803-9736-3
Type
conf
DOI
10.1109/ICOSP.2006.345838
Filename
4129215
Link To Document