Neural network architectures for vector prediction
| Autor: | Lin-Cheng Wang, Nasser M. Nasrabadi, Syed A. Rizvi |
|---|---|
| Rok vydání: | 1996 |
| Předmět: |
Statistics::Theory
Radial basis function network Artificial neural network Computer science business.industry Computer Science::Neural and Evolutionary Computation Vector quantization Pattern recognition Linear prediction Perceptron Statistics::Machine Learning Nonlinear system Probabilistic neural network Multilayer perceptron Statistics::Methodology Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | Proceedings of the IEEE. 84:1513-1528 |
| ISSN: | 0018-9219 |
| DOI: | 10.1109/5.537115 |
| Popis: | A vector predictor is an integral part of a predictive vector quantization coding scheme. The conventional techniques for designing a nonlinear predictor are extremely complex and suboptimal due to the absence of a suitable model for the source data. We investigated several neural network architectures that can be used to implement a nonlinear vector predictor, including the multilayer perceptron, the functional link network and the radial basis function network. We also evaluated and compared the performance of these neural network predictors with that of a linear vector predictor. Our experimental results show that a neural network predictor can predict the blocks containing edges with a higher accuracy than a linear predictor. However, the performance of a neural network predictor is comparable to that of a linear predictor for predicting the stationary and shade blocks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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