| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 10360454 | Pattern Recognition | 2005 | 4 Pages |
Abstract
A learning vector quantization (LVQ) algorithm called harmonic to minimum LVQ algorithm (H2M-LVQ)1 is presented to tackle the initialization sensitiveness problem associated with the original generalized LVQ (GLVQ) algorithm. Experimental results show superior performance of the H2M-LVQ algorithm over the GLVQ and one of its variants on several datasets.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
A.K. Qin, P.N. Suganthan,