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Figure 3 | BMC Bioinformatics

Figure 3

From: Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

Figure 3

Dependence of classifier performance on amount of training data. The average performances of neural network (filled circle), SVM (open diamond), AdaBoost (filled triangle), Bagging (filled square), Mixtures-of-Experts (filled diamond), and majority-voting ensemble (open square) classifiers are shown as a function of the amount of training data given to the classifier. Average performance is defined as the average fraction of images in ten (2D) or eleven (3D) classes that were correctly classified over ten cross-validation trials. A) Results for 2D images using feature set SLF13. B) Results for 3D images using feature set SLF10.

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