A mathematical analysis of a class of learning vector quantization (LVQ) algorithms is presented. Using an appropriate time-coordinate transformation, we show that the LVQ algorithms under consideration can be transformed into linear time-varying stochastic difference equations. Using this fact, we apply stochastic Lyapunov stability arguments, and we prove that the LVQ algorithms under consideration do indeed converge, provided that some appropriate conditions hold.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/83.480771 | DOI Listing |
Publication Analysis
Top Keywords
lvq algorithms
12
class learning
8
learning vector
8
vector quantization
8
algorithms consideration
8
convergence properties
4
properties class
4
algorithms
4
quantization algorithms
4
algorithms mathematical
4
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!