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• W2023163512 abstract "We study how closely the optimal Bayes error rate can be approximately reached using a classification algorithm that computes a classifier by minimizing a convex upper bound of the classification error function. The measurement of closeness is characterized by the loss function used in the estimation. We show that such a classification scheme can be generally regarded as a (nonmaximum-likelihood) conditional in-class probability estimate, and we use this analysis to compare various convex loss functions that have appeared in the literature. Furthermore, the theoretical insight allows us to design good loss functions with desirable properties. Another aspect of our analysis is to demonstrate the consistency of certain classification methods using convex risk minimization. This study sheds light on the good performance of some recently proposed linear classification methods including boosting and support vector machines. It also shows their limitations and suggests possible improvements." @default.
• W2023163512 created "2016-06-24" @default.
• W2023163512 creator A5037709424 @default.
• W2023163512 date "2004-02-01" @default.
• W2023163512 modified "2023-10-18" @default.
• W2023163512 title "Statistical behavior and consistency of classification methods based on convex risk minimization" @default.
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• W2023163512 doi "https://doi.org/10.1214/aos/1079120130" @default.
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