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On-line learning with malicious noise and the closure algorithm

Electronic Colloquium on Computational Complexity (ECCC), no. 71 (2000): 83-99

Cited by: 5|Views89
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Abstract

We investigate a variant of the on‐line learning model for classes of \{0,1\}‐valued functions (concepts) in which the labels of a certain amount of the input instances are corrupted by adversarial noise. We propose an extension of a general learning strategy, known as “Closure Algorithm”, to this noise model, and show a worst&d...More

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