Skip to main content

Table 1 Notation and Symbols

From: An active learning based classification strategy for the minority class problem: application to histopathology annotation

Symbol Description Symbol Description
r R Dataset of image patches t {0, , T} Iteration of ActiveLearn
Str, Ste Unlabeled training, testing pools Φ Training methodology
S t E , S ^ t E Eligible samples, annotated samples S t , Φ tr Samples labeled via Φ at t
T t Fuzzy classifier using S t , Φ tr k1,t, k2,t Number of samples in S t E from ω1, ω2
M Number of votes used to generate T t ω1, ω2 Possible classes of r
τ Confidence margin r ω 1 Membership of r in class ω1
θ Classifier-dependent threshold for T t k 1 ^ , k 2 ^ Number of samples in S ^ t E from ω1, ω2
p t (r ω1) Probability of observing r ω1 N t Samples added to training set at t
P Δ Model confidence P ^ t Probability of observing k 1 ^ samples
A t Accuracy of trained classifier at t Total training cost after T iterations
  1. List of the commonly used notation and symbols.