๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Fast competitive learning with classified learning rates for vector quantization

โœ Scribed by Chang Wook Kim; Seongwon Cho; Choong Woong Lee


Publisher
Elsevier Science
Year
1995
Tongue
English
Weight
710 KB
Volume
6
Category
Article
ISSN
0923-5965

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

Fast image vector quantization using a m
โœ Robert Li; Earnest Sherrod; Jung Kim; Gao Pan ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 259 KB ๐Ÿ‘ 2 views

The basic goal of image compression through vector generates the address of the codevector specified by Q(x); and quantization (VQ) is to reduce the bit rate for transmission or data a decoder, which uses this address to generate the codevector y. storage while maintaining an acceptable fidelity or