How to Analyze Learning Models with Real Data?
Abstract
Replica methods are a key theoretical tool for analyzing learning curves. However, traditional calculations often assume idealized data distributions. By employing a variational Gaussian approximation for the replicated system within a grand-canonical formalism, the idealized data average can be replaced with empirical averages from real datasets, enabling estimation of the predictive error of parametric models trained on real data.
Fields of Study & Subjects
Neural Networks and Applications
Theoretical and Computational Physics
Machine Learning and Algorithms
Publication details
- Journal / source
- JPSJ News and Comments
- Volume
- 23
- Publication date
- 15 January 2026
- Language
- EN