Supervised Growing Neural Gas
| Autor: | Carlos Henrique Quartucci Forster, Klaifer Garcia |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Linde–Buzo–Gray algorithm
Learning vector quantization Neural gas Artificial neural network Computer science business.industry Supervised learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Codebook Vector quantization Pattern recognition Machine learning computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Multilayer perceptron Artificial intelligence business computer |
| Zdroj: | Intelligent Data Engineering and Automated Learning-IDEAL 2012 ISBN: 9783642326387 IDEAL |
| DOI: | 10.1007/978-3-642-32639-4_61 |
| Popis: | We present a new approach to supervised vector quantization inspired on growing neural gas network. An advantage of the new method is that it reduces the need for prior knowledge about the problem under study because it is able to determine at runtime the size of the codebook. Another advantage is that the training is less dependent on the initial state of the codebook vectors in contrast to methods like Learning Vector Quantization. Finally, it is shown that for some real datasets the classification performance is superior to other methods of supervised vector quantization. |
| Databáze: | OpenAIRE |
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