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On-line learning with malicious noise and the closure algorithm
[chapter]
1994
Lecture Notes in Computer Science
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-case mistake bound of m+(d+1)K for learning an arbitrary intersection-closed concept class C, where K is the number of noisy labels, d is a combinatorial parameter
doi:10.1007/3-540-58520-6_67
fatcat:3opzv24ju5a7ppjgahpfmsvde4