{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:CQ7UWYEW4IT2QHUYATIITWZTTE","short_pith_number":"pith:CQ7UWYEW","schema_version":"1.0","canonical_sha256":"143f4b6096e227a81e9804d089db33991f5bfaee6c1dc288e7304b0de63980f3","source":{"kind":"arxiv","id":"1604.03200","version":1},"attestation_state":"computed","paper":{"title":"Efficient Classification of Multi-Labelled Text Streams by Clashing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Ilias Flaounas, Nello Cristianini, Ricardo \\~Nanculef","submitted_at":"2016-04-12T01:52:38Z","abstract_excerpt":"We present a method for the classification of multi-labelled text documents explicitly designed for data stream applications that require to process a virtually infinite sequence of data using constant memory and constant processing time. Our method is composed of an online procedure used to efficiently map text into a low-dimensional feature space and a partition of this space into a set of regions for which the system extracts and keeps statistics used to predict multi-label text annotations. Documents are fed into the system as a sequence of words, mapped to a region of the partition, and 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":"1604.03200","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-04-12T01:52:38Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6073cccb942b3b8ad8b49e7b0d27d281120e83c2d5f635c36feca51e9adde924","abstract_canon_sha256":"64b3a6d0a5dcae50a0227a19a29253f6d9f2d87f4ae25d09d0f41ce3504d8aa8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:16.177651Z","signature_b64":"O/DmdDV75LGSWBTLnQBv/s/OUC/9effJqtaS0UrEwOCUxQQFS4hGN5886DHbuG9h02IZf3HTtNIHi1S9r5BaAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"143f4b6096e227a81e9804d089db33991f5bfaee6c1dc288e7304b0de63980f3","last_reissued_at":"2026-05-18T01:17:16.177003Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:16.177003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Classification of Multi-Labelled Text Streams by Clashing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Ilias Flaounas, Nello Cristianini, Ricardo \\~Nanculef","submitted_at":"2016-04-12T01:52:38Z","abstract_excerpt":"We present a method for the classification of multi-labelled text documents explicitly designed for data stream applications that require to process a virtually infinite sequence of data using constant memory and constant processing time. Our method is composed of an online procedure used to efficiently map text into a low-dimensional feature space and a partition of this space into a set of regions for which the system extracts and keeps statistics used to predict multi-label text annotations. Documents are fed into the system as a sequence of words, mapped to a region of the partition, and a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.03200","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":"1604.03200","created_at":"2026-05-18T01:17:16.177122+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.03200v1","created_at":"2026-05-18T01:17:16.177122+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.03200","created_at":"2026-05-18T01:17:16.177122+00:00"},{"alias_kind":"pith_short_12","alias_value":"CQ7UWYEW4IT2","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_16","alias_value":"CQ7UWYEW4IT2QHUY","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_8","alias_value":"CQ7UWYEW","created_at":"2026-05-18T12:30:09.641336+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/CQ7UWYEW4IT2QHUYATIITWZTTE","json":"https://pith.science/pith/CQ7UWYEW4IT2QHUYATIITWZTTE.json","graph_json":"https://pith.science/api/pith-number/CQ7UWYEW4IT2QHUYATIITWZTTE/graph.json","events_json":"https://pith.science/api/pith-number/CQ7UWYEW4IT2QHUYATIITWZTTE/events.json","paper":"https://pith.science/paper/CQ7UWYEW"},"agent_actions":{"view_html":"https://pith.science/pith/CQ7UWYEW4IT2QHUYATIITWZTTE","download_json":"https://pith.science/pith/CQ7UWYEW4IT2QHUYATIITWZTTE.json","view_paper":"https://pith.science/paper/CQ7UWYEW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.03200&json=true","fetch_graph":"https://pith.science/api/pith-number/CQ7UWYEW4IT2QHUYATIITWZTTE/graph.json","fetch_events":"https://pith.science/api/pith-number/CQ7UWYEW4IT2QHUYATIITWZTTE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CQ7UWYEW4IT2QHUYATIITWZTTE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CQ7UWYEW4IT2QHUYATIITWZTTE/action/storage_attestation","attest_author":"https://pith.science/pith/CQ7UWYEW4IT2QHUYATIITWZTTE/action/author_attestation","sign_citation":"https://pith.science/pith/CQ7UWYEW4IT2QHUYATIITWZTTE/action/citation_signature","submit_replication":"https://pith.science/pith/CQ7UWYEW4IT2QHUYATIITWZTTE/action/replication_record"}},"created_at":"2026-05-18T01:17:16.177122+00:00","updated_at":"2026-05-18T01:17:16.177122+00:00"}