Name
Title | ||
|---|---|---|
Potential Suport Vector Machines And Self-Organizing Maps For Phytoplankton Discrimination |
Abstract | ||
|---|---|---|
Fluorescence spectroscopy is a powerful technique usually used to evaluate phytoplankton marine environments. In this study, a kernel method (Potential Support Vector Machine, P-SVM) is presented, evaluating its capability to achieve phytoplankton classification from its fluorescence spectra. Different phytoplankton species were studied, and their fluorescence spectra were acquired in laboratory. In a previous study working with Self-Organizing Maps (SOM), it was proved with experimental data from laboratory that excitation spectra were more discriminative than emission spectra. It was also shown that using some preprocessing techniques, such as derivative analysis, the classification performance from emission fluorescence data can be improved. The classification results were encouraging to keep working with emission fluorescence, and herein we present a comparison between P-SVM and SOM for this goal. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596808 | 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 |
Keywords | Field | DocType |
fluorescence,indexes,spectra,marine engineering,optimization,fluorescence spectroscopy,self organizing maps,hyperspectral imaging,support vector machines,emission spectra,kernel,support vector machine,kernel method | Kernel (linear algebra),Pattern recognition,Emission spectrum,Computer science,Support vector machine,Fluorescence spectroscopy,Hyperspectral imaging,Self-organizing map,Artificial intelligence,Kernel method,Discriminative model,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ismael F. Aymerich | 1 | 3 | 2.18 |
| Jaume Piera | 2 | 7 | 4.50 |
| Aureli Soria-Frisch | 3 | 83 | 11.13 |