Title :
Energy generalized LVQ with relevance factors
Author :
A. Cataron;R. Andonie
Author_Institution :
Dept. of Electron. & Comput., Transylvania Univ. of Brasov, Romania
fDate :
6/26/1905 12:00:00 AM
Abstract :
Input feature ranking and selection represent a necessary preprocessing stage in classification, especially when one is required to manage large quantities of data. We introduce a weighted generalized LVQ algorithm, called energy generalized relevance LVQ (EGRLVQ), based on the Onicescu´s informational energy. EGRLVQ is an incremental learning algorithm for supervised classification and feature ranking.
Keywords :
"Clustering algorithms","Prototypes","Euclidean distance","Computer science","Vector quantization","Bayesian methods","Training data","Iterative algorithms","Convergence","Data structures"
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1380159
