{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Z5OIQ6Z7AHY7QTFLXADQVAT6UO","short_pith_number":"pith:Z5OIQ6Z7","schema_version":"1.0","canonical_sha256":"cf5c887b3f01f1f84cabb8070a827ea3ab9782828ecb080e08b6bbb3f596925c","source":{"kind":"arxiv","id":"1902.00045","version":1},"attestation_state":"computed","paper":{"title":"Gaussian Conditional Random Fields for Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrija Petrovi\\'c, Boris Deliba\\v{s}i\\'c, Milo\\v{s} Jovanovi\\'c, Mladen Nikoli\\'c","submitted_at":"2019-01-31T19:33:13Z","abstract_excerpt":"Gaussian conditional random fields (GCRF) are a well-known used structured model for continuous outputs that uses multiple unstructured predictors to form its features and at the same time exploits dependence structure among outputs, which is provided by a similarity measure. In this paper, a Gaussian conditional random fields model for structured binary classification (GCRFBC) is proposed. The model is applicable to classification problems with undirected graphs, intractable for standard classification CRFs. The model representation of GCRFBC is extended by latent variables which yield some a"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1902.00045","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-31T19:33:13Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"ede445c4d11fb55d4a000aadba2ebd79908cc6ab51c5af2f14fdb34dbb8eb2eb","abstract_canon_sha256":"4e97f7567da790d3d564339e0363a54ccfb54a7a267342d35cdc9ceefbaa32c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:58.380217Z","signature_b64":"sFlcsvmljl8iV9Xe7QBMOE8/0CWY3XpSOXx9YBi/BFRV4H9DED2Gg9YM9nMey2Lo1wSM3ZtJrOuUTVJ/9u7rBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf5c887b3f01f1f84cabb8070a827ea3ab9782828ecb080e08b6bbb3f596925c","last_reissued_at":"2026-05-17T23:54:58.379718Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:58.379718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gaussian Conditional Random Fields for Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrija Petrovi\\'c, Boris Deliba\\v{s}i\\'c, Milo\\v{s} Jovanovi\\'c, Mladen Nikoli\\'c","submitted_at":"2019-01-31T19:33:13Z","abstract_excerpt":"Gaussian conditional random fields (GCRF) are a well-known used structured model for continuous outputs that uses multiple unstructured predictors to form its features and at the same time exploits dependence structure among outputs, which is provided by a similarity measure. In this paper, a Gaussian conditional random fields model for structured binary classification (GCRFBC) is proposed. The model is applicable to classification problems with undirected graphs, intractable for standard classification CRFs. The model representation of GCRFBC is extended by latent variables which yield some a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.00045","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1902.00045","created_at":"2026-05-17T23:54:58.379801+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.00045v1","created_at":"2026-05-17T23:54:58.379801+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.00045","created_at":"2026-05-17T23:54:58.379801+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z5OIQ6Z7AHY7","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z5OIQ6Z7AHY7QTFL","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z5OIQ6Z7","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z5OIQ6Z7AHY7QTFLXADQVAT6UO","json":"https://pith.science/pith/Z5OIQ6Z7AHY7QTFLXADQVAT6UO.json","graph_json":"https://pith.science/api/pith-number/Z5OIQ6Z7AHY7QTFLXADQVAT6UO/graph.json","events_json":"https://pith.science/api/pith-number/Z5OIQ6Z7AHY7QTFLXADQVAT6UO/events.json","paper":"https://pith.science/paper/Z5OIQ6Z7"},"agent_actions":{"view_html":"https://pith.science/pith/Z5OIQ6Z7AHY7QTFLXADQVAT6UO","download_json":"https://pith.science/pith/Z5OIQ6Z7AHY7QTFLXADQVAT6UO.json","view_paper":"https://pith.science/paper/Z5OIQ6Z7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.00045&json=true","fetch_graph":"https://pith.science/api/pith-number/Z5OIQ6Z7AHY7QTFLXADQVAT6UO/graph.json","fetch_events":"https://pith.science/api/pith-number/Z5OIQ6Z7AHY7QTFLXADQVAT6UO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z5OIQ6Z7AHY7QTFLXADQVAT6UO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z5OIQ6Z7AHY7QTFLXADQVAT6UO/action/storage_attestation","attest_author":"https://pith.science/pith/Z5OIQ6Z7AHY7QTFLXADQVAT6UO/action/author_attestation","sign_citation":"https://pith.science/pith/Z5OIQ6Z7AHY7QTFLXADQVAT6UO/action/citation_signature","submit_replication":"https://pith.science/pith/Z5OIQ6Z7AHY7QTFLXADQVAT6UO/action/replication_record"}},"created_at":"2026-05-17T23:54:58.379801+00:00","updated_at":"2026-05-17T23:54:58.379801+00:00"}