Alternative learning vector quantization
โ Scribed by Kuo-Lung Wu; Miin-Shen Yang
- Publisher
- Elsevier Science
- Year
- 2006
- Tongue
- English
- Weight
- 597 KB
- Volume
- 39
- Category
- Article
- ISSN
- 0031-3203
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
We compare a number of training algorithms for competitive learning networks applied to the problem of vector quantization for data compression. A new competitive-learning algorithm based on the "conscience" learning method is introduced. The performance of competitive learning neural networks and t
In this paper we present a necessary and sufficient condition for global optimality of unsupervised Learning Vector Quantization (LVQ) in kernel space. In particular, we generalize the results presented for expansive and competitive learning for vector quantization in Euclidean space, to the general
Artificial neural networks (ANNs) may be of significant value in extracting vegetation type information in complex vegetation mapping problems, particularly in coastal wetland environments. Unsupervised, self-organizing ANNs have not been employed as frequently as supervised ANNs for vegetation mapp