{"paper":{"title":"Fast Cross-Validation for Incremental Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Andr\\'as Gy\\\"orgy, Csaba Szepesv\\'ari, Pooria Joulani","submitted_at":"2015-06-30T23:30:28Z","abstract_excerpt":"Cross-validation (CV) is one of the main tools for performance estimation and parameter tuning in machine learning.   The general recipe for computing CV estimate   is to run a learning algorithm separately   for each CV fold, a computationally expensive process.   In this paper, we propose a new approach to reduce   the computational burden of CV-based performance estimation.   As opposed to all previous attempts, which are specific to a particular   learning model or problem domain, we propose a general method applicable   to a large class of incremental learning algorithms,   which are uniq"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.00066","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"}