{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:YAA27YA3WQ2ZXWNHCFB3YSEDIJ","short_pith_number":"pith:YAA27YA3","canonical_record":{"source":{"id":"1203.2879","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2012-03-13T18:02:05Z","cross_cats_sorted":[],"title_canon_sha256":"a4ff76202907996bbb513ad9a537089dade99cb1acfc4fee1ebc92b5a50b044a","abstract_canon_sha256":"8a9d3c89da034c601ffe8e08fcc714d790217e1013b7b99f574686f984b2d4a6"},"schema_version":"1.0"},"canonical_sha256":"c001afe01bb4359bd9a71143bc48834271bc09e657327121575d5bce8b9dc439","source":{"kind":"arxiv","id":"1203.2879","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1203.2879","created_at":"2026-05-18T04:00:14Z"},{"alias_kind":"arxiv_version","alias_value":"1203.2879v1","created_at":"2026-05-18T04:00:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1203.2879","created_at":"2026-05-18T04:00:14Z"},{"alias_kind":"pith_short_12","alias_value":"YAA27YA3WQ2Z","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_16","alias_value":"YAA27YA3WQ2ZXWNH","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_8","alias_value":"YAA27YA3","created_at":"2026-05-18T12:27:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:YAA27YA3WQ2ZXWNHCFB3YSEDIJ","target":"record","payload":{"canonical_record":{"source":{"id":"1203.2879","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2012-03-13T18:02:05Z","cross_cats_sorted":[],"title_canon_sha256":"a4ff76202907996bbb513ad9a537089dade99cb1acfc4fee1ebc92b5a50b044a","abstract_canon_sha256":"8a9d3c89da034c601ffe8e08fcc714d790217e1013b7b99f574686f984b2d4a6"},"schema_version":"1.0"},"canonical_sha256":"c001afe01bb4359bd9a71143bc48834271bc09e657327121575d5bce8b9dc439","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:00:14.315207Z","signature_b64":"xnRwRRk1IjJlO0VOqPgmjploPQvyHiSrZZh3iNF69nIWutnG8dt6Enc09LPtbI7+LDrlJTSRZAzDTnwAt5KWAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c001afe01bb4359bd9a71143bc48834271bc09e657327121575d5bce8b9dc439","last_reissued_at":"2026-05-18T04:00:14.314365Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:00:14.314365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1203.2879","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:00:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B5v57USrPHoj28oe01+bhogG/ZbtOgQVi7zpXYckIxODyB1OtNeeXuXab39+1ZckCJM8QynY/zWbVV0t3NyLBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T14:32:18.116960Z"},"content_sha256":"639c1550f6b710c931c2feb18dae41a377286ac7a50142c4439b9aef909e5d6c","schema_version":"1.0","event_id":"sha256:639c1550f6b710c931c2feb18dae41a377286ac7a50142c4439b9aef909e5d6c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:YAA27YA3WQ2ZXWNHCFB3YSEDIJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An imputation method for estimating the learning curve in classification problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Eric B. Laber, Kerby Shedden, Yang Yang","submitted_at":"2012-03-13T18:02:05Z","abstract_excerpt":"The learning curve expresses the error rate of a predictive modeling procedure as a function of the sample size of the training dataset. It typically is a decreasing, convex function with a positive limiting value. An estimate of the learning curve can be used to assess whether a modeling procedure should be expected to become substantially more accurate if additional training data become available. This article proposes a new procedure for estimating learning curves using imputation. We focus on classification, although the idea is applicable to other predictive modeling settings. Simulation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1203.2879","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:00:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bamNhUf6fSAXfZ6YhpDvEub65wmQMm+mU1wqWy3n6enbvDJuS4Qlbex15BIOMgKPDAKbe2jRM7cxUp4eVSUtAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T14:32:18.117672Z"},"content_sha256":"1a745ac616955c2fc77aeb96361880490740747a4a49f13be6894a021a567018","schema_version":"1.0","event_id":"sha256:1a745ac616955c2fc77aeb96361880490740747a4a49f13be6894a021a567018"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YAA27YA3WQ2ZXWNHCFB3YSEDIJ/bundle.json","state_url":"https://pith.science/pith/YAA27YA3WQ2ZXWNHCFB3YSEDIJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YAA27YA3WQ2ZXWNHCFB3YSEDIJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-29T14:32:18Z","links":{"resolver":"https://pith.science/pith/YAA27YA3WQ2ZXWNHCFB3YSEDIJ","bundle":"https://pith.science/pith/YAA27YA3WQ2ZXWNHCFB3YSEDIJ/bundle.json","state":"https://pith.science/pith/YAA27YA3WQ2ZXWNHCFB3YSEDIJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YAA27YA3WQ2ZXWNHCFB3YSEDIJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:YAA27YA3WQ2ZXWNHCFB3YSEDIJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8a9d3c89da034c601ffe8e08fcc714d790217e1013b7b99f574686f984b2d4a6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2012-03-13T18:02:05Z","title_canon_sha256":"a4ff76202907996bbb513ad9a537089dade99cb1acfc4fee1ebc92b5a50b044a"},"schema_version":"1.0","source":{"id":"1203.2879","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1203.2879","created_at":"2026-05-18T04:00:14Z"},{"alias_kind":"arxiv_version","alias_value":"1203.2879v1","created_at":"2026-05-18T04:00:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1203.2879","created_at":"2026-05-18T04:00:14Z"},{"alias_kind":"pith_short_12","alias_value":"YAA27YA3WQ2Z","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_16","alias_value":"YAA27YA3WQ2ZXWNH","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_8","alias_value":"YAA27YA3","created_at":"2026-05-18T12:27:27Z"}],"graph_snapshots":[{"event_id":"sha256:1a745ac616955c2fc77aeb96361880490740747a4a49f13be6894a021a567018","target":"graph","created_at":"2026-05-18T04:00:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The learning curve expresses the error rate of a predictive modeling procedure as a function of the sample size of the training dataset. It typically is a decreasing, convex function with a positive limiting value. An estimate of the learning curve can be used to assess whether a modeling procedure should be expected to become substantially more accurate if additional training data become available. This article proposes a new procedure for estimating learning curves using imputation. We focus on classification, although the idea is applicable to other predictive modeling settings. Simulation ","authors_text":"Eric B. Laber, Kerby Shedden, Yang Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2012-03-13T18:02:05Z","title":"An imputation method for estimating the learning curve in classification problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1203.2879","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:639c1550f6b710c931c2feb18dae41a377286ac7a50142c4439b9aef909e5d6c","target":"record","created_at":"2026-05-18T04:00:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8a9d3c89da034c601ffe8e08fcc714d790217e1013b7b99f574686f984b2d4a6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2012-03-13T18:02:05Z","title_canon_sha256":"a4ff76202907996bbb513ad9a537089dade99cb1acfc4fee1ebc92b5a50b044a"},"schema_version":"1.0","source":{"id":"1203.2879","kind":"arxiv","version":1}},"canonical_sha256":"c001afe01bb4359bd9a71143bc48834271bc09e657327121575d5bce8b9dc439","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c001afe01bb4359bd9a71143bc48834271bc09e657327121575d5bce8b9dc439","first_computed_at":"2026-05-18T04:00:14.314365Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:00:14.314365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xnRwRRk1IjJlO0VOqPgmjploPQvyHiSrZZh3iNF69nIWutnG8dt6Enc09LPtbI7+LDrlJTSRZAzDTnwAt5KWAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T04:00:14.315207Z","signed_message":"canonical_sha256_bytes"},"source_id":"1203.2879","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:639c1550f6b710c931c2feb18dae41a377286ac7a50142c4439b9aef909e5d6c","sha256:1a745ac616955c2fc77aeb96361880490740747a4a49f13be6894a021a567018"],"state_sha256":"662a60b1b9586a83d8ec85dde3d84e7b5fbfca803d3caed0869f11309056cb49"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tkYiGy2BtFlBDB5ZsZBc1KSUicHq9oSJJZwSdfyZsjxOFmOgYwfnUiTxU3kE1mG2rzRIucdid90swY6nIpajCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T14:32:18.121367Z","bundle_sha256":"8b7abb963a1c5a2bb2e40b0b42349cf28f39fc89fefdf9a4a9ab6e0013d2fdec"}}