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Table 4 The classification results of multiple view features using MKL with one kind of kernel

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 88.48% 11.35% 0.886 89.58% 10.21% 0.897 88.55% 11.18% 0.887
  10 88.75% 11.04% 0.889 89.67% 10.16% 0.898 89.12% 10.65% 0.892
  100 88.58% 11.2% 0.887 89.39% 10.44% 0.895 88.42% 11.41% 0.885
ZooScan 1 83.32% 11.74% 0.857 83.94% 12.61% 0.856 81.78% 15.53% 0.831
  10 86.26% 10.01% 0.881 86.74% 11.76% 0.875 83.98% 15.28% 0.843
  100 86.6% 9.92% 0.883 86.79% 11.63% 0.876 84.86% 19.13% 0.828
Kaggle 1 78.46% 17.41% 0.805 80.39% 16.76% 0.818 78.09% 19.24% 0.794
  10 82.95% 16.42% 0.833 82.6% 15.62% 0.835 81.32% 17.66% 0.818
  100 82.97% 16.84% 0.831 82.11% 15.82% 0.831 79.68% 19.1% 0.803
  1. The entries in boldface indicate the best classification results with the highest F measure