๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Alternative learning vector quantization

โœ 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.


๐Ÿ“œ SIMILAR VOLUMES


Competitive learning algorithms for vect
โœ Stanley C. Ahalt; Ashok K. Krishnamurthy; Prakoon Chen; Douglas E. Melton ๐Ÿ“‚ Article ๐Ÿ“… 1990 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 889 KB

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

Expansive competitive learning for kerne
โœ Davide Bacciu; Antonina Starita ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 778 KB

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

Fuzzy learning vector quantization for h
โœ Anthony M. Filippi; John R. Jensen ๐Ÿ“‚ Article ๐Ÿ“… 2006 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 795 KB

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