| Autor: |
Tamás, Ambrus, Csáji, Balázs Csanád |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Control Systems Letters, Volume 6, 2022, pp. 860-865 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/LCSYS.2021.3087409 |
| Popis: |
In this paper we suggest two statistical hypothesis tests for the regression function of binary classification based on conditional kernel mean embeddings. The regression function is a fundamental object in classification as it determines both the Bayes optimal classifier and the misclassification probabilities. A resampling based framework is presented and combined with consistent point estimators of the conditional kernel mean map, in order to construct distribution-free hypothesis tests. These tests are introduced in a flexible manner allowing us to control the exact probability of type I error for any sample size. We also prove that both proposed techniques are consistent under weak statistical assumptions, i.e., the type II error probabilities pointwise converge to zero. |
| Databáze: |
arXiv |
| Externí odkaz: |
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