Paper Info
Title
Learning Vector Quantization With Local Subspace Classifier
Abstract
In this paper generalized learning vector quantization (GLVQ) with local subspace classifier (LSC) is proposed for achieving high accuracy with a small memory requirement. In a training phase, the k-closest prototypes to an input training sample are moved by the same update rule of GLVQ for reducing the number of misclassification on training samples. In a test phase, a test sample is classified by LSC with trained prototypes. Experimental results on a handwritten digit show that the proposed learning rule outperforms other classifiers such as the original GLVQ algorithm.
Year
DOI
Venue
2008
10.1109/ICPR.2008.4761816
19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6
Keywords
Field
DocType
learning artificial intelligence,prototypes,learning vector quantization,artificial neural networks,accuracy,classification algorithms
Distance measurement,Pattern recognition,Subspace topology,Computer science,Learning vector quantization,Speech recognition,Vector quantization,Learning rule,Artificial intelligence,Classifier (linguistics),Statistical classification,Artificial neural network
Conference
ISSN
Citations 
PageRank 
1051-4651
0
0.34
References 
Authors
10
1
Name
Order
Citations
PageRank
Seiji Hotta164.98