The key theorem of learning theory about examples corrupted by noise
Ming-Hu Ha, Junhua Li, Jia Li, Xizhao Wang · 2005
Statistical learning theory has investigated the conditions for consistency of the learning processes based on the empirical risk minimization induction principle. However, it deals with the unrealistic, i.e. noise-free case. We give the key theorem when the outputs are corrupted by noise.