Support Vector Machine Classi ers as Applied to AVIRIS Data

@inproceedings{Gualtieri1999SupportVM,
  title={Support Vector Machine Classi ers as Applied to AVIRIS Data},
  author={J. Anthony Gualtieri and Samir Chettri and Robert F. Cromp and Laurence F. Johnson},
  year={1999}
}
Traditionally, classi ers model the underlying density of the various classes and then nd a separating surface. However density estimation in high-dimensional spaces su ers from the Hughes e ect (Hughes, 1968), (Landgrebe, 1999): For a xed amount of training data the classi cation accuracy as a function of number of bands reaches a maximum and then declines, because there is limited amount of training data to estimate the large number of parameters needed. Thus usually, a feature selection step… CONTINUE READING
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