{"paper":{"title":"Bayesian Network Classifiers in a High Dimensional Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dietrich von Rosen, Tatjana Pavlenko","submitted_at":"2012-12-12T15:57:54Z","abstract_excerpt":"We present a growing dimension asymptotic formalism. The perspective in this paper is classification theory and we show that it can  accommodate probabilistic  networks classifiers, including naive Bayes model and its augmented version. When represented as a Bayesian network these  classifiers have an important advantage: The corresponding discriminant function turns out to be  a specialized case of a  generalized additive model, which makes it possible to get closed form expressions for the asymptotic misclassification  probabilities used here  as a measure of classification accuracy.  Moreov"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.0593","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}