Name
Abstract | ||
|---|---|---|
In the context of supervised vectorial quantization (VQ) learning algorithms, we present an algorithm (SLTI) that exploits the self-organizing properties
arising from a particular process of temporal inhibition of the winning units in competitive learning. This exploitation consists
of establishing independence capabilities in the initialization of the prototypes (weight vectors), together with generalization capabilities, which to a certain extent solve some of the critical problems
involved in the use of conventional algorithms such as LVQs and DSM. Another original aspect of this paper is the inclusion
in SLTI of a simple rule for prototype adaptation, which incorporates certain useful features that make possible to plan the
configuration of the SLTI parameters with specific goals in order to approach classification tasks of varied complexity and
natures (versatility). This versatility is experimentally demonstrated with synthetic data comprising non linearly-separable classes, overlapping
classes and interlaced classes with a certain degree of overlap.
|
Year | DOI | Venue |
|---|---|---|
1999 | 10.1007/BFb0098219 | IWANN (1) |
Keywords | Field | DocType |
supervised vq learning,temporal inhibition,competitive learning,synthetic data,self organization | Competitive learning,Pattern recognition,Computer science,Weight,Exploit,Synthetic data,Vector quantization,Artificial intelligence,Adaptive algorithm,Initialization,Quantization (signal processing),Machine learning | Conference |
Volume | ISSN | ISBN |
1606 | 0302-9743 | 3-540-66069-0 |
Citations | PageRank | References |
1 | 0.43 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Martín-Smith | 1 | 29 | 4.04 |
| Francisco J. Pelayo | 2 | 303 | 34.63 |
| Eduardo Ros | 3 | 1100 | 86.00 |
| Alberto Prieto | 4 | 84 | 11.99 |