DocumentCode
2361246
Title
Application of the HLVQ neural network to hand-written digit recognition
Author
Solaiman, B. ; Autret, Y.
Author_Institution
Ecole Nat. Superieure des Telecommun. de Bretagne, Brest, France
fYear
1994
fDate
6-8 Sep 1994
Firstpage
384
Lastpage
393
Abstract
In this work, the handwritten digit recognition problem is studied. Self organizing feature maps are mainly considered. The unsupervised Kohonen as well as the hybrid learning vector quantization (HLVQ) algorithms are applied. The main objective is to obtain a topology preserving map having high recognition rates. This is essentially due to the fact that this kind of maps is very useful in realising results interpretations and in the definition of a rejection strategy during the recognition phase
Keywords
character recognition; self-organising feature maps; topology; unsupervised learning; vector quantisation; handwritten digit recognition; hybrid learning vector quantization; neural network; self organizing feature maps; topology preserving map; unsupervised Kohonen; Bayesian methods; Character recognition; Clustering algorithms; Multi-layer neural network; Network topology; Neural networks; Neurons; Prototypes; Self organizing feature maps; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing [1994] IV. Proceedings of the 1994 IEEE Workshop
Conference_Location
Ermioni
Print_ISBN
0-7803-2026-3
Type
conf
DOI
10.1109/NNSP.1994.366030
Filename
366030
Link To Document