On the Posterior Probability estimate of the error rate of nonparametric classification rules

By Gábor Lugosi, M. Pawlak. 1993

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Analysis of classifiers’ robustness to adversarial perturbations (2015, background): “For example, Łojasiewicz (1961) and later Luo and Pang (1994) have shown that, quite remarkably, assumption (A) holds for the general class of analytic functions.7 In Ng and Zheng (2003), (A) is shown to hold with γ = 1 for piecewise linear functions. In Luo and Luo (1994), error bounds on polynomia”
Stability and Generalization (2002, methodology): “The idea to smooth error estimators to decrease their variance is not new and it has even been used in conjunction with McDiarmid’s inequality by Lugosi and Pawlak (1994) in order to derive error bounds for certain algorithms.”
Bandwidth choice for nonparametric classification (2005, methodology): “…including techniques based on nonparametric density estimation; Pawlak (1993) proposes kernelbased classification rules for use with incomplete data; Lugosi and Pawlak (1994) describe properties of a posterior-probability estimator of classification error for nonparametric classifiers; Ancukiewicz…”