Name
Title | ||
|---|---|---|
A Genetically Optimized Artificial Neural Network Structure for Feature Extraction and Classification of Vascular Tissue Fluorescence Spectrums |
Abstract | ||
|---|---|---|
The optimization of Neural Network structures for feature extraction and classification by employing Genetic Algorithms is addressed here. More precisely, a non-linear filter based on High Order Neural Networks (HONN) whose weights are updated by stable learning laws is used to extract the characteristic features of fluorescence spectrums correspond to human tissue samples of different stares. The process is optimized by a generic algorithm which maximizes the separability of different classes. The features are then classified with a Multi-Layer Perceptron (MLP). The high rates of success together with the small time needed to analyze the signals, proves our method very attractive for real time applications. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/CAMP.2000.875964 | Padova |
Keywords | Field | DocType |
spectrum,computer networks,neural nets,artificial neural networks,feature extraction,multi layer perceptron,generic algorithm,genetics,classification,genetic algorithm,genetic engineering,neural networks,fluorescence,neural network,genetic algorithms,artificial neural network | Pattern recognition,Computer science,Feature extraction,Multilayer perceptron,Artificial intelligence,Artificial neural network,Perceptron,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
0-7695-0740-9 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| George A. Rovithakis | 1 | 749 | 45.73 |
| Michail Maniadakis | 2 | 10 | 1.92 |
| M. Zervakis | 3 | 191 | 20.57 |