Paper Info
Title
An informational energy LVQ approach for feature ranking
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 LVQ algo- rithm, called Energy Relevance LVQ (ERLVQ), based on Onicescu's in- formational energy (10). ERLVQ is an incremental learning algorithm for supervised classification and feature ranking.
Year
Venue
Field
2004
ESANN
Learning to rank,Ranking SVM,Pattern recognition,Computer science,Feature ranking,Learning vector quantization,Preprocessor,Artificial intelligence,Incremental learning algorithm,Machine learning
DocType
Citations 
PageRank 
Conference
5
0.58
References 
Authors
7
2
Name
Order
Citations
PageRank
Razvan Andonie111717.71
Angel Cataron2123.31