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Table 1 Comparison of the characteristics of the classifiers used in this study. The results are derived from the data in Figures 2-4.

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

Classifier

Ability to generate nonlinear decision boundary

Ability to learn well from limited training data

Insensitivity to outliers in training data

Insensitivity to uninformative features

Log information content* (2D/3D)

Neural Networks

Low

High

Medium

Medium

10.0/10.0

Exponential-rbf-kernel SVM

High

High

High

Low

14.2/13.9

AdaBoost

Medium

Med

High

High

13.5/13.4

Bagging

Medium

Low

High

High

14.8/12.0

Mixtures-of-Experts

Medium

Low

High

High

13.5/13.5

Majority-voting Ensemble

Medium

High

High

High

14.7/14.6

  1. * The natural logarithmic of the information content was calculated as described in the Methods section for the feature set SLF13 (2D) and SLF10 (3D). The classifiers were configured as detailed in Table 2.