Years and Authors of Summarized Original Work
1993; Kearns, Li
1999; Cesa-Bianchi, Dichterman, Fischer, Shamir, Simon
Problem Definition
This problem is concerned with PAC learning of concept classes when training examples are affected by malicious errors. The PAC (probably approximately correct) model of learning (also known as the distribution-free model of learning) was introduced by Valiant [13]. This model makes the idealized assumption that error-free training examples are generated from the same distribution which is then used to evaluate the learned hypothesis. In many environments, however, there is some chance that an erroneous example is given to the learning algorithm. The malicious noise model – again introduced by Valiant [14] – extends the PAC model by allowing example errors of any kind: it makes no assumptions on the nature of the errors that occur. In this sense the malicious noise model is a worst-case model of errors, in which errors may be generated by an...
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Recommended Reading
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Auer, P. (2015). Learning with Malicious Noise. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-3-642-27848-8_198-2
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DOI: https://doi.org/10.1007/978-3-642-27848-8_198-2
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