{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:SXVZUYH47RE4TWD5V43K7Q343Z","short_pith_number":"pith:SXVZUYH4","schema_version":"1.0","canonical_sha256":"95eb9a60fcfc49c9d87daf36afc37cde62bddf4a531d57d433ba028d9b2ab216","source":{"kind":"arxiv","id":"1009.3346","version":1},"attestation_state":"computed","paper":{"title":"Conditional Random Fields and Support Vector Machines: A Hybrid Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Mark D. Reid, Qinfeng Shi, Tiberio Caetano","submitted_at":"2010-09-17T06:47:25Z","abstract_excerpt":"We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs). We provide a sufficient condition for when the hybrid loss is Fisher consistent for classification. This condition depends on a measure of dominance between labels - specifically, the gap in per observation probabilities between the most likely labels. We also prove Fisher consistency is necessary for parametric consistency when learning models such as CRFs.\n  We demonstrate"},"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":"1009.3346","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2010-09-17T06:47:25Z","cross_cats_sorted":[],"title_canon_sha256":"00ab663dc09d4df5f00c7fe5341b83bda68beeeda5d590d326da3e0e91d25c4f","abstract_canon_sha256":"1adfcae03d7824a6c98e405924dd025311c371f6f2cd3b1cbd59af3d0df640c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:40:45.909490Z","signature_b64":"zKAU/fOCBYrfW4M7D7Zkx2Z77GfRxRIjcNuHG3iL5acadrS10aMv4SfbXSpGS5bAehUDx82cYuBGTII4mH9cAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95eb9a60fcfc49c9d87daf36afc37cde62bddf4a531d57d433ba028d9b2ab216","last_reissued_at":"2026-05-18T04:40:45.908641Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:40:45.908641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conditional Random Fields and Support Vector Machines: A Hybrid Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Mark D. Reid, Qinfeng Shi, Tiberio Caetano","submitted_at":"2010-09-17T06:47:25Z","abstract_excerpt":"We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs). We provide a sufficient condition for when the hybrid loss is Fisher consistent for classification. This condition depends on a measure of dominance between labels - specifically, the gap in per observation probabilities between the most likely labels. We also prove Fisher consistency is necessary for parametric consistency when learning models such as CRFs.\n  We demonstrate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1009.3346","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":"1009.3346","created_at":"2026-05-18T04:40:45.908775+00:00"},{"alias_kind":"arxiv_version","alias_value":"1009.3346v1","created_at":"2026-05-18T04:40:45.908775+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1009.3346","created_at":"2026-05-18T04:40:45.908775+00:00"},{"alias_kind":"pith_short_12","alias_value":"SXVZUYH47RE4","created_at":"2026-05-18T12:26:13.927090+00:00"},{"alias_kind":"pith_short_16","alias_value":"SXVZUYH47RE4TWD5","created_at":"2026-05-18T12:26:13.927090+00:00"},{"alias_kind":"pith_short_8","alias_value":"SXVZUYH4","created_at":"2026-05-18T12:26:13.927090+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/SXVZUYH47RE4TWD5V43K7Q343Z","json":"https://pith.science/pith/SXVZUYH47RE4TWD5V43K7Q343Z.json","graph_json":"https://pith.science/api/pith-number/SXVZUYH47RE4TWD5V43K7Q343Z/graph.json","events_json":"https://pith.science/api/pith-number/SXVZUYH47RE4TWD5V43K7Q343Z/events.json","paper":"https://pith.science/paper/SXVZUYH4"},"agent_actions":{"view_html":"https://pith.science/pith/SXVZUYH47RE4TWD5V43K7Q343Z","download_json":"https://pith.science/pith/SXVZUYH47RE4TWD5V43K7Q343Z.json","view_paper":"https://pith.science/paper/SXVZUYH4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1009.3346&json=true","fetch_graph":"https://pith.science/api/pith-number/SXVZUYH47RE4TWD5V43K7Q343Z/graph.json","fetch_events":"https://pith.science/api/pith-number/SXVZUYH47RE4TWD5V43K7Q343Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SXVZUYH47RE4TWD5V43K7Q343Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SXVZUYH47RE4TWD5V43K7Q343Z/action/storage_attestation","attest_author":"https://pith.science/pith/SXVZUYH47RE4TWD5V43K7Q343Z/action/author_attestation","sign_citation":"https://pith.science/pith/SXVZUYH47RE4TWD5V43K7Q343Z/action/citation_signature","submit_replication":"https://pith.science/pith/SXVZUYH47RE4TWD5V43K7Q343Z/action/replication_record"}},"created_at":"2026-05-18T04:40:45.908775+00:00","updated_at":"2026-05-18T04:40:45.908775+00:00"}