{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:32TWGEIPCIGYW4QUZA5RMXIDUO","short_pith_number":"pith:32TWGEIP","canonical_record":{"source":{"id":"1108.5244","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-08-26T05:38:58Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"ed1be0d8ce6a5ec29a6cd3760bcbfb4b6fb0ce9fa608afcb407bc85f42ded4b5","abstract_canon_sha256":"7ea36c39ebf04144344116de305c1cad183cdab2a58a2ee55e3375ec7af51224"},"schema_version":"1.0"},"canonical_sha256":"dea763110f120d8b7214c83b165d03a3a804be91b700cf36668df329eddbe5e2","source":{"kind":"arxiv","id":"1108.5244","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1108.5244","created_at":"2026-05-18T02:58:43Z"},{"alias_kind":"arxiv_version","alias_value":"1108.5244v3","created_at":"2026-05-18T02:58:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1108.5244","created_at":"2026-05-18T02:58:43Z"},{"alias_kind":"pith_short_12","alias_value":"32TWGEIPCIGY","created_at":"2026-05-18T12:26:18Z"},{"alias_kind":"pith_short_16","alias_value":"32TWGEIPCIGYW4QU","created_at":"2026-05-18T12:26:18Z"},{"alias_kind":"pith_short_8","alias_value":"32TWGEIP","created_at":"2026-05-18T12:26:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:32TWGEIPCIGYW4QUZA5RMXIDUO","target":"record","payload":{"canonical_record":{"source":{"id":"1108.5244","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-08-26T05:38:58Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"ed1be0d8ce6a5ec29a6cd3760bcbfb4b6fb0ce9fa608afcb407bc85f42ded4b5","abstract_canon_sha256":"7ea36c39ebf04144344116de305c1cad183cdab2a58a2ee55e3375ec7af51224"},"schema_version":"1.0"},"canonical_sha256":"dea763110f120d8b7214c83b165d03a3a804be91b700cf36668df329eddbe5e2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:58:43.599124Z","signature_b64":"eQVDwqmQJ6HYTa+bjgyOEL4mrFQMjV5dhbg7jLETdR6gf7t2/IbA+5XxzUQ+afM92ZGr07BlsyHtCRkZbuppBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dea763110f120d8b7214c83b165d03a3a804be91b700cf36668df329eddbe5e2","last_reissued_at":"2026-05-18T02:58:43.598308Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:58:43.598308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1108.5244","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:58:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mi4gDrSPbEZRRuK+OL9b1cIuyAS9YV5BqRdLWROsGSruU7s7CEqu+KhSxvcAs7Y3S8+eqRhxXAq29UKPcHW3Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T09:34:30.380870Z"},"content_sha256":"00404f9b753aa962ca80f24c999ff3d9ca03075409f1cc2f86a7e513aae21269","schema_version":"1.0","event_id":"sha256:00404f9b753aa962ca80f24c999ff3d9ca03075409f1cc2f86a7e513aae21269"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:32TWGEIPCIGYW4QUZA5RMXIDUO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.ML","authors_text":"Shuichi Kawano","submitted_at":"2011-08-26T05:38:58Z","abstract_excerpt":"This article addresses the problem of classification method based on both labeled and unlabeled data, where we assume that a density function for labeled data is different from that for unlabeled data. We propose a semi-supervised logistic regression model for classification problem along with the technique of covariate shift adaptation. Unknown parameters involved in proposed models are estimated by regularization with EM algorithm. A crucial issue in the modeling process is the choices of tuning parameters in our semi-supervised logistic models. In order to select the parameters, a model sel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1108.5244","kind":"arxiv","version":3},"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":"2026-05-18T02:58:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rFTQKQdoJ0LbhBAFtVTpy6ZON/yIv1bHdh09PQdnD/MS8ROqB2UdJ38it6dCAMeHUcZBqcPXDK7vX8RE/1RzDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T09:34:30.381224Z"},"content_sha256":"67d67faa326b505c0b553081b8120ae49e92011b51a3a8351c8ce1076c718e72","schema_version":"1.0","event_id":"sha256:67d67faa326b505c0b553081b8120ae49e92011b51a3a8351c8ce1076c718e72"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/32TWGEIPCIGYW4QUZA5RMXIDUO/bundle.json","state_url":"https://pith.science/pith/32TWGEIPCIGYW4QUZA5RMXIDUO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/32TWGEIPCIGYW4QUZA5RMXIDUO/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":"2026-06-01T09:34:30Z","links":{"resolver":"https://pith.science/pith/32TWGEIPCIGYW4QUZA5RMXIDUO","bundle":"https://pith.science/pith/32TWGEIPCIGYW4QUZA5RMXIDUO/bundle.json","state":"https://pith.science/pith/32TWGEIPCIGYW4QUZA5RMXIDUO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/32TWGEIPCIGYW4QUZA5RMXIDUO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:32TWGEIPCIGYW4QUZA5RMXIDUO","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":"7ea36c39ebf04144344116de305c1cad183cdab2a58a2ee55e3375ec7af51224","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-08-26T05:38:58Z","title_canon_sha256":"ed1be0d8ce6a5ec29a6cd3760bcbfb4b6fb0ce9fa608afcb407bc85f42ded4b5"},"schema_version":"1.0","source":{"id":"1108.5244","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1108.5244","created_at":"2026-05-18T02:58:43Z"},{"alias_kind":"arxiv_version","alias_value":"1108.5244v3","created_at":"2026-05-18T02:58:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1108.5244","created_at":"2026-05-18T02:58:43Z"},{"alias_kind":"pith_short_12","alias_value":"32TWGEIPCIGY","created_at":"2026-05-18T12:26:18Z"},{"alias_kind":"pith_short_16","alias_value":"32TWGEIPCIGYW4QU","created_at":"2026-05-18T12:26:18Z"},{"alias_kind":"pith_short_8","alias_value":"32TWGEIP","created_at":"2026-05-18T12:26:18Z"}],"graph_snapshots":[{"event_id":"sha256:67d67faa326b505c0b553081b8120ae49e92011b51a3a8351c8ce1076c718e72","target":"graph","created_at":"2026-05-18T02:58:43Z","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":"This article addresses the problem of classification method based on both labeled and unlabeled data, where we assume that a density function for labeled data is different from that for unlabeled data. We propose a semi-supervised logistic regression model for classification problem along with the technique of covariate shift adaptation. Unknown parameters involved in proposed models are estimated by regularization with EM algorithm. A crucial issue in the modeling process is the choices of tuning parameters in our semi-supervised logistic models. In order to select the parameters, a model sel","authors_text":"Shuichi Kawano","cross_cats":["stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-08-26T05:38:58Z","title":"Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1108.5244","kind":"arxiv","version":3},"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":"sha256:00404f9b753aa962ca80f24c999ff3d9ca03075409f1cc2f86a7e513aae21269","target":"record","created_at":"2026-05-18T02:58:43Z","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":"7ea36c39ebf04144344116de305c1cad183cdab2a58a2ee55e3375ec7af51224","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-08-26T05:38:58Z","title_canon_sha256":"ed1be0d8ce6a5ec29a6cd3760bcbfb4b6fb0ce9fa608afcb407bc85f42ded4b5"},"schema_version":"1.0","source":{"id":"1108.5244","kind":"arxiv","version":3}},"canonical_sha256":"dea763110f120d8b7214c83b165d03a3a804be91b700cf36668df329eddbe5e2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dea763110f120d8b7214c83b165d03a3a804be91b700cf36668df329eddbe5e2","first_computed_at":"2026-05-18T02:58:43.598308Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:58:43.598308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eQVDwqmQJ6HYTa+bjgyOEL4mrFQMjV5dhbg7jLETdR6gf7t2/IbA+5XxzUQ+afM92ZGr07BlsyHtCRkZbuppBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:58:43.599124Z","signed_message":"canonical_sha256_bytes"},"source_id":"1108.5244","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:00404f9b753aa962ca80f24c999ff3d9ca03075409f1cc2f86a7e513aae21269","sha256:67d67faa326b505c0b553081b8120ae49e92011b51a3a8351c8ce1076c718e72"],"state_sha256":"9afac3f66e5f729fd05a80e1fff8999e21b4be7c2d69134137d44a2120c20edb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eECbhDr14tYKzRsrF2YurqpiK5/A1owIHILl8fSpedTIB3x3t21U0T6A6tAdeHktEJP1GpWEutk3uQBzlQm7Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T09:34:30.383219Z","bundle_sha256":"c27b0bf67bd0318dd8db745a7a723ee12f6cd8cd444a202d498ed5bff2c05a77"}}