On-line learning with malicious noise and the closure algorithm [chapter]

Peter Auer, Nicoló Cesa-Bianchi
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
more » ... g C's complexity, and m is the worst-case mistake bound of the Closure Algorithm for learning C in the noise-free model. For several concept classes our extended Closure Algorithm is efficient and can tolerate a noise rate up to the information-theoretic upper bound. Finally, we show how to efficiently turn any algorithm for the on-line noise model into a learning algorithm for the PAC model with malicious noise.
doi:10.1007/3-540-58520-6_67 fatcat:3opzv24ju5a7ppjgahpfmsvde4