Name
Abstract | ||
|---|---|---|
Despite the higher efficiency obtained by some algorithms (quasi-Newton methods, cascade correlation, etc.) in feedforward neural networks, faster learning methods such as those based on codebook vectors are still needed. We propose to perform feature selection in codebook based methods to improve their accuracy. However, we define a neural network with an exact and fast parallel implementation of the nearest network rule which allows previous feature selection by means of a pruning method. Moreover, we apply this feature selection algorithm upon another codebook based classifier - the Kohonen's linear vector quantization |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/IJCNN.1999.832662 | IJCNN |
Keywords | Field | DocType |
feature extraction,feedforward neural nets,learning (artificial intelligence),pattern classification,codebook vectors,feature selection,feedforward neural networks,learning,pruning algorithm,quasi newton method,neural networks,neural network,databases,intelligent networks,learning artificial intelligence,vector quantization,sampling methods,feedforward neural network | Pattern recognition,Linde–Buzo–Gray algorithm,Feature selection,Computer science,Learning vector quantization,Probabilistic neural network,Time delay neural network,Vector quantization,Artificial intelligence,Neural gas,Machine learning,Codebook | Conference |
Volume | ISSN | ISBN |
3 | 1098-7576 | 0-7803-5529-6 |
Citations | PageRank | References |
1 | 0.35 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mar Abad Grau, M. | 1 | 1 | 0.35 |
| Molinero, L.D.H. | 2 | 1 | 0.35 |