On the Validation of Gibbs Algorithms: Training Datasets, Test Datasets and their Aggregation

Autor:	Perlaza, Samir M., Esnaola, Iñaki, Bisson, Gaetan, Poor, H. Vincent
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Information Theory Mathematics - Probability Mathematics - Statistics Theory
Druh dokumentu:	Working Paper
Popis:	The dependence on training data of the Gibbs algorithm (GA) is analytically characterized. By adopting the expected empirical risk as the performance metric, the sensitivity of the GA is obtained in closed form. In this case, sensitivity is the performance difference with respect to an arbitrary alternative algorithm. This description enables the development of explicit expressions involving the training errors and test errors of GAs trained with different datasets. Using these tools, dataset aggregation is studied and different figures of merit to evaluate the generalization capabilities of GAs are introduced. For particular sizes of such datasets and parameters of the GAs, a connection between Jeffrey's divergence, training and test errors is established. Comment: In Proc. IEEE International Symposium on Information Theory (ISIT), Taipei, Taiwan, Jun., 2023. arXiv admin note: text overlap with arXiv:2211.06617
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.12380 Zobrazit plný text záznamu View this record from Arxiv