An algorithm is presented for deriving an optimal features classified with a support vector machine. The approach is
based on direct objective optimization which is approximated by the selection of appropriate features as the SVM
learning predictor in a regularized learning framework. To process the regularized learning, a genetic method provides a
learning rule for in an outer loop of an iteration, while at each iteration training predictor model using gradient descent is
to gradually added the feature into improving the existing model. The inner loop is heuristic to perform support vector
machine training and provide support vector coefficients on which the gradient descent depends. The experiment was
conduced on the Airborne Visible/Infrared Imaging Spectrometer(AVIRIS) data for classification. The result shows that
the feature selection of spectral dimension and support vector machine are jointly optimized.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.