SRBFSpherical Radial Basis Function (computer graphics)
SRBFSynthetic Resin Bonded Fabric
SRBFSynthetic Resin Bonded Fiberglass
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References in periodicals archive ?
The training samples of the two zones are learned using OAO-SMO, OAASmO, DAG-SMO, ECC-SMO, and BDT-SMO, respectively, based on the same kernel (SRBF).
Land usages of the two zones are classified using linear, polynomial, sigmoid, SRBF, and CRBF-based BDT-SMO classifiers, respectively.
From Tables 6 and 7, it can be seen that the CRBF-based BDT-SMO classifiers have the highest overall accuracy (at 85.43% and 86.15%, 1.97% and 2.10% higher than the SRBF kernel), average accuracy (at 85.67% and 85.52%, 2.01% and 2.15% higher than the SRBF kernel), and Kappa coefficient values (0.8411 and 0.8483).