Skip to main content
Article thumbnail
Location of Repository

Learning Vector Quantization With Alternative Distance Criteria

By J. S. Sánchez and F. Pla

Abstract

An adaptive algorithm for training of a Nearest Neighbour (NN) classifier is developed in this paper. This learning rule has got some similarity to the well-known LVQ method, but using the nearest centroid neighbourhood concept to estimate optimal locations of the codebook vectors. The aim of this approach is to improve the performance of the standard LVQ algorithms when using a very small codebook. The behaviour of the learning technique proposed here is experimentally compared to those of the plain k-NN decision rule and the LVQ algorithms. 1

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.517
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://ieeexplore.ieee.org/iel... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.