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The average time consuming for classification on the TerraSAR-X images of Tianjin and Rosenheim areas using the baseline PL+SVM, and our GL+LpcSVM is provided in Table 4.

Our approach partially relates to the work of Kermany et al. [30], where the kNN algorithm was applied for classification on water consumption data, in order to detect irregular consumer behavior.

Table 12 Comparison of classifiers: classification accuracies (acc), mean precision (pre), and mean recall (rec) for classification on AllInst test data with the NoLyr feature set for the V3 and A3 tasks.

Table 9 Lassification accuracies (acc), mean precision (pre), and mean recall (rec) for classification on AllInst test data against different attribute subsets for the V3 and A3 tasks, SVM.

Supposing we get N states, and the dimension of Markov transition probabilities will be N × N, which should be as small as possible considering the calculation complex and time consumption for classification on the premise of not affecting the recognition results too much.

However, the method is for classification on large image blocks and does not deal with small tissue cores or IHC images.

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Korbar, B. et al. Deep learning for classification of colorectal polyps on whole-slide images.

So we developed a new method for classification of EGG based on DWT and ANN.

Decree on criteria for classification of genetically modified organisms according to safety classes.

A terminology for classification of the genes found on sets of related genomes has been developed.

In a clinical situation, the best biomarker signature is the one least dependent for classification accuracy on any kind of homogenous selection criteria.

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