General bounds on statistical query learning and PAC learning with noise via hypothesis boosting
The authors have derived the general bounds on the complexity of learning in the statistical query model and in the PAC model with classification noise. By the way the authors have shown a general scheme for boosting the accuracy of weak SQ learning algorithms in order to prove that the weak of SQ learning is equivalent to strong SQ learning.
scientific article; zbMATH DE number 1220698
scientific article