PAC Learning with Nasty Noise

We introduce a new model for learning in the presence of noise, which we call the Nasty Noise model. This model generalizes previously considered models of learning with noise. The learning process in this model, which is a variant of the PAC model, proceeds as follows: Suppose that the learning algorithm during its execution asks for m examples. The examples that the algorithm gets are generated by a nasty adversary that works according to a following steps. First, the adversary chooses m examples (independently) according to the fixed (but unknown to the learning algorithm) distribution D as in the PAC-model. Then the powerful adversary, upon seeing the specific m examples that were chosen (and using his knowledge of the target function, the distribution D and the learning algorithm), is allowed to remove a fraction of the examples at its choice, and replace these examples by the same number of arbitrary examples of its choice; the m modified examples are then given to the learni...

• Type: Conference
• Year: 1999
• Where: ALT
• Authors: Nader H. Bshouty, Nadav Eiron, Eyal Kushilevitz
• Keywords: ALT 1999, Learning Algorithm, Machine Learning, Nasty Adversary, Nasty Noise Model