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Table 3 The classification results of multiple view features using SVM

From: Automatic plankton image classification combining multiple view features via multiple kernel learning

Datasets C Gaussian Polynomial Linear
   R 1−P F measure R 1−P F measure R 1−P F measure
WHOI 1 84% 15.43% 0.843 88.97% 10.95% 0.89 86.45% 13.41% 0.865
  10 88.94% 11% 0.89 89.45% 10.47% 0.895 88.12% 11.78% 0.882
  100 89.57% 10.3% 0.896 88.42% 11.46% 0.885 86.33% 13.59% 0.864
ZooScan 1 79.65% 16.06% 0.817 82.45% 15.99% 0.832 79.91% 18.14% 0.809
  10 85.39% 13.2% 0.861 84.14% 15.22% 0.845 85.52% 16.01% 0.847
  100 84.87% 13.62% 0.856 83.04% 16.02% 0.835 82.27% 18.23% 0.82
Kaggle 1 77.26% 18.96% 0.791 77.48% 19.6% 0.789 71.32% 25.09% 0.731
  10 82.41% 16.33% 0.83 80.7% 18.08% 0.813 78.44% 20.63% 0.789
  100 82.09% 18.89% 0.816 79.01% 19.73% 0.796 78.1% 22.05% 0.78
  1. The entries in boldface indicate the best classification results with the highest F measure