{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:BUPFUD3JK5PVZT2PYVZPQSNSGE","short_pith_number":"pith:BUPFUD3J","schema_version":"1.0","canonical_sha256":"0d1e5a0f69575f5ccf4fc572f849b2310351345e63bae10a1146d12f731f4211","source":{"kind":"arxiv","id":"1211.2190","version":4},"attestation_state":"computed","paper":{"title":"Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.ML"],"primary_cat":"cs.LG","authors_text":"David Luengo, Jesse Read, Luca Martino","submitted_at":"2012-11-09T17:21:48Z","abstract_excerpt":"Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance - at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest- performing methods for multi-label classification (MLC), a particular case of "},"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":"1211.2190","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-11-09T17:21:48Z","cross_cats_sorted":["stat.CO","stat.ML"],"title_canon_sha256":"f7671ba9c428c33ee5473c4f13f87864318a4cd245b630def0f0a6b3acb59388","abstract_canon_sha256":"15062ba840f9c31cebf011dc12cea20986e12d580c1c04e06adac275391865a9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:51:42.534640Z","signature_b64":"ZD2u3UCcQLeX12Y67B5FwFHKWx7u61ftFANPJyVF3P6rT6rXKAgr5ODHEUgJu9ICLwobxiIpxYVUAucefTTgAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d1e5a0f69575f5ccf4fc572f849b2310351345e63bae10a1146d12f731f4211","last_reissued_at":"2026-05-18T02:51:42.534154Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:51:42.534154Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.ML"],"primary_cat":"cs.LG","authors_text":"David Luengo, Jesse Read, Luca Martino","submitted_at":"2012-11-09T17:21:48Z","abstract_excerpt":"Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance - at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest- performing methods for multi-label classification (MLC), a particular case of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1211.2190","kind":"arxiv","version":4},"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":"1211.2190","created_at":"2026-05-18T02:51:42.534229+00:00"},{"alias_kind":"arxiv_version","alias_value":"1211.2190v4","created_at":"2026-05-18T02:51:42.534229+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1211.2190","created_at":"2026-05-18T02:51:42.534229+00:00"},{"alias_kind":"pith_short_12","alias_value":"BUPFUD3JK5PV","created_at":"2026-05-18T12:27:01.376967+00:00"},{"alias_kind":"pith_short_16","alias_value":"BUPFUD3JK5PVZT2P","created_at":"2026-05-18T12:27:01.376967+00:00"},{"alias_kind":"pith_short_8","alias_value":"BUPFUD3J","created_at":"2026-05-18T12:27:01.376967+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/BUPFUD3JK5PVZT2PYVZPQSNSGE","json":"https://pith.science/pith/BUPFUD3JK5PVZT2PYVZPQSNSGE.json","graph_json":"https://pith.science/api/pith-number/BUPFUD3JK5PVZT2PYVZPQSNSGE/graph.json","events_json":"https://pith.science/api/pith-number/BUPFUD3JK5PVZT2PYVZPQSNSGE/events.json","paper":"https://pith.science/paper/BUPFUD3J"},"agent_actions":{"view_html":"https://pith.science/pith/BUPFUD3JK5PVZT2PYVZPQSNSGE","download_json":"https://pith.science/pith/BUPFUD3JK5PVZT2PYVZPQSNSGE.json","view_paper":"https://pith.science/paper/BUPFUD3J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1211.2190&json=true","fetch_graph":"https://pith.science/api/pith-number/BUPFUD3JK5PVZT2PYVZPQSNSGE/graph.json","fetch_events":"https://pith.science/api/pith-number/BUPFUD3JK5PVZT2PYVZPQSNSGE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BUPFUD3JK5PVZT2PYVZPQSNSGE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BUPFUD3JK5PVZT2PYVZPQSNSGE/action/storage_attestation","attest_author":"https://pith.science/pith/BUPFUD3JK5PVZT2PYVZPQSNSGE/action/author_attestation","sign_citation":"https://pith.science/pith/BUPFUD3JK5PVZT2PYVZPQSNSGE/action/citation_signature","submit_replication":"https://pith.science/pith/BUPFUD3JK5PVZT2PYVZPQSNSGE/action/replication_record"}},"created_at":"2026-05-18T02:51:42.534229+00:00","updated_at":"2026-05-18T02:51:42.534229+00:00"}