Information theoretic perspective on sample complexity.
Neural Netw
; 167: 445-449, 2023 Oct.
Article
em En
| MEDLINE
| ID: mdl-37673030
ABSTRACT
The statistical supervised learning framework assumes an input-output set with a joint probability distribution that is reliably represented by the training dataset. The learning system is then required to output a prediction rule learned from the training dataset's input-output pairs. In this work, we investigate the relationship between the sample complexity, the empirical risk and the generalization error based on the asymptotic equipartition property (AEP) (Shannon, 1948). We provide theoretical guarantees for reliable learning under the information-theoretic AEP, with respect to the generalization error and the sample size in different settings.
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1
Base de dados:
MEDLINE
Assunto principal:
Probabilidade
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article