Publication | Closed Access
A bound on the label complexity of agnostic active learning
247
Citations
14
References
2007
Year
Unknown Venue
Artificial IntelligenceAgnostic Pac ModelEngineeringMachine LearningData ScienceA2 AlgorithmComputational Learning TheoryAlgorithmic LearningComputational ComplexityStatistical InferenceComputer ScienceStatistical Learning TheoryAlgorithmic Information TheoryLabel ComplexitySupervised Learning
We study the label complexity of pool-based active learning in the agnostic PAC model. Specifically, we derive general bounds on the number of label requests made by the A2 algorithm proposed by Balcan, Beygelzimer & Langford (Balcan et al., 2006). This represents the first nontrivial general-purpose upper bound on label complexity in the agnostic PAC model.
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