title: Statistical mechanics of learning: a variational approach for real data.
abstract: Using a variational technique, we generalize the statistical physics approach of learning from random examples to make it applicable to real data. We demonstrate the validity and relevance of our method by computing approximate estimators for generalization errors that are based on training data alone.
authors.affiliation: Neural Computing Research Group, School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, United Kingdom.
authors.forename: Dörthe
authors.initials: D
authors.lastname: Malzahn
authors.forename: Manfred
authors.initials: M
authors.lastname: Opper
mesh_terms: D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013223:Statistics as Topic / Q000379:methods*