{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:7PGIWZDLQ46TTG2JCFTGGHFFE6","short_pith_number":"pith:7PGIWZDL","schema_version":"1.0","canonical_sha256":"fbcc8b646b873d399b491166631ca527b6750d86989f6fac404c9dc7e34a0c0d","source":{"kind":"arxiv","id":"1408.0838","version":1},"attestation_state":"computed","paper":{"title":"Estimating Maximally Probable Constrained Relations by Mathematical Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bjoern Andres, Lizhen Qu","submitted_at":"2014-08-04T23:30:20Z","abstract_excerpt":"Estimating a constrained relation is a fundamental problem in machine learning. Special cases are classification (the problem of estimating a map from a set of to-be-classified elements to a set of labels), clustering (the problem of estimating an equivalence relation on a set) and ranking (the problem of estimating a linear order on a set). We contribute a family of probability measures on the set of all relations between two finite, non-empty sets, which offers a joint abstraction of multi-label classification, correlation clustering and ranking by linear ordering. Estimating (learning) a ma"},"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":"1408.0838","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-08-04T23:30:20Z","cross_cats_sorted":["cs.NA","math.OC","stat.ML"],"title_canon_sha256":"5b722158ff7d991064c3125e992b76ed1b1da2f8988d28d5e6428c30b444463b","abstract_canon_sha256":"0964fbe76309bbc0a95296620d58a80508ab871bfe5161ecd087fb51ed59d887"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:45:50.270122Z","signature_b64":"kPIpxk2afsmWFFfof032VevA0nTqlnSs6c6lld1KipAYEwvvYb9jhwzYXeVQaW25tRXM/8KbuyCuzxqzNO0NAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbcc8b646b873d399b491166631ca527b6750d86989f6fac404c9dc7e34a0c0d","last_reissued_at":"2026-05-18T02:45:50.269547Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:45:50.269547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating Maximally Probable Constrained Relations by Mathematical Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bjoern Andres, Lizhen Qu","submitted_at":"2014-08-04T23:30:20Z","abstract_excerpt":"Estimating a constrained relation is a fundamental problem in machine learning. Special cases are classification (the problem of estimating a map from a set of to-be-classified elements to a set of labels), clustering (the problem of estimating an equivalence relation on a set) and ranking (the problem of estimating a linear order on a set). We contribute a family of probability measures on the set of all relations between two finite, non-empty sets, which offers a joint abstraction of multi-label classification, correlation clustering and ranking by linear ordering. Estimating (learning) a ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.0838","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":"1408.0838","created_at":"2026-05-18T02:45:50.269617+00:00"},{"alias_kind":"arxiv_version","alias_value":"1408.0838v1","created_at":"2026-05-18T02:45:50.269617+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1408.0838","created_at":"2026-05-18T02:45:50.269617+00:00"},{"alias_kind":"pith_short_12","alias_value":"7PGIWZDLQ46T","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_16","alias_value":"7PGIWZDLQ46TTG2J","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_8","alias_value":"7PGIWZDL","created_at":"2026-05-18T12:28:19.803747+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/7PGIWZDLQ46TTG2JCFTGGHFFE6","json":"https://pith.science/pith/7PGIWZDLQ46TTG2JCFTGGHFFE6.json","graph_json":"https://pith.science/api/pith-number/7PGIWZDLQ46TTG2JCFTGGHFFE6/graph.json","events_json":"https://pith.science/api/pith-number/7PGIWZDLQ46TTG2JCFTGGHFFE6/events.json","paper":"https://pith.science/paper/7PGIWZDL"},"agent_actions":{"view_html":"https://pith.science/pith/7PGIWZDLQ46TTG2JCFTGGHFFE6","download_json":"https://pith.science/pith/7PGIWZDLQ46TTG2JCFTGGHFFE6.json","view_paper":"https://pith.science/paper/7PGIWZDL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1408.0838&json=true","fetch_graph":"https://pith.science/api/pith-number/7PGIWZDLQ46TTG2JCFTGGHFFE6/graph.json","fetch_events":"https://pith.science/api/pith-number/7PGIWZDLQ46TTG2JCFTGGHFFE6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7PGIWZDLQ46TTG2JCFTGGHFFE6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7PGIWZDLQ46TTG2JCFTGGHFFE6/action/storage_attestation","attest_author":"https://pith.science/pith/7PGIWZDLQ46TTG2JCFTGGHFFE6/action/author_attestation","sign_citation":"https://pith.science/pith/7PGIWZDLQ46TTG2JCFTGGHFFE6/action/citation_signature","submit_replication":"https://pith.science/pith/7PGIWZDLQ46TTG2JCFTGGHFFE6/action/replication_record"}},"created_at":"2026-05-18T02:45:50.269617+00:00","updated_at":"2026-05-18T02:45:50.269617+00:00"}