Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
The Journal of Machine Learning Research
Almost-everywhere algorithmic stability and generalization error
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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This note mainly focuses on a theoretical analysis of the generalization ability of classification learning algorithm. The explicit bound is derived on the relative difference between the generalization error and leave-one-out error for classification learning algorithm under the condition of leave-one-out stability by using Markov's inequality, and then this bound is used to estimate the generalization error of classification learning algorithm. We compare the result in this paper with previous results in the end.