Title :
A hybrid algorithm (HLVQ) combining unsupervised and supervised learning approaches
Author :
Solaiman, B. ; Mouchot, M.C. ; Maillard, E.
Author_Institution :
Ecole Nat. Superieure des Telecommun. de Bretagne, Brest, France
fDate :
27 Jun-2 Jul 1994
Abstract :
A new algorithm called HLVQ (Hybrid Learning Vector Quantization) is developed in this study. This algorithm is based on the combined use of the unsupervised learning algorithm of the self-organizing feature map, and a modified version of the LVQ2 supervised learning algorithm. The main objective is to obtain a classifier preserving topology mapping and performing as well as the LVQ2 classifier
Keywords :
iterative methods; learning (artificial intelligence); pattern classification; self-organising feature maps; topology; vector quantisation; LVQ2 classifier; hybrid learning vector quantization; self-organizing feature map; supervised learning; topology mapping; unsupervised learning; Decision making; Diseases; Joining processes; Pattern matching; Pattern recognition; Stability; Supervised learning; Topology; Unsupervised learning; Vector quantization;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374424
