{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:KG37JDUH3Q7TBF6PVJIHVGFAB7","short_pith_number":"pith:KG37JDUH","schema_version":"1.0","canonical_sha256":"51b7f48e87dc3f3097cfaa507a98a00fd6fa3a611e259a80bd3f424ce9acfd86","source":{"kind":"arxiv","id":"1507.07146","version":1},"attestation_state":"computed","paper":{"title":"A Framework of Sparse Online Learning and Its Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dayong Wang, Peilin Zhao, Pengcheng Wu, Steven C.H. Hoi","submitted_at":"2015-07-25T22:53:31Z","abstract_excerpt":"The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, high sparsity, and high class-imbalance. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some e"},"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":"1507.07146","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-25T22:53:31Z","cross_cats_sorted":[],"title_canon_sha256":"9ef5b646c2c0bfb4b97c53a0d70d8e23a476b8191cc8e994fa030d656168c800","abstract_canon_sha256":"f78bd76942fbf0bcf8ebd5714ba279bc677d7a9fdd3f9d9cca25abd6977c023a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:17.103919Z","signature_b64":"zXM6uTLtHsQIyzGx5w05fKXMdfWY9MF3GlsAL+T4GRtwvNOwt0IoTveBMJKdncyzp8RFBurp8m8hsX0SS3QZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51b7f48e87dc3f3097cfaa507a98a00fd6fa3a611e259a80bd3f424ce9acfd86","last_reissued_at":"2026-05-18T01:36:17.103444Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:17.103444Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Framework of Sparse Online Learning and Its Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dayong Wang, Peilin Zhao, Pengcheng Wu, Steven C.H. Hoi","submitted_at":"2015-07-25T22:53:31Z","abstract_excerpt":"The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, high sparsity, and high class-imbalance. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.07146","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":"1507.07146","created_at":"2026-05-18T01:36:17.103515+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.07146v1","created_at":"2026-05-18T01:36:17.103515+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.07146","created_at":"2026-05-18T01:36:17.103515+00:00"},{"alias_kind":"pith_short_12","alias_value":"KG37JDUH3Q7T","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_16","alias_value":"KG37JDUH3Q7TBF6P","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_8","alias_value":"KG37JDUH","created_at":"2026-05-18T12:29:27.538025+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/KG37JDUH3Q7TBF6PVJIHVGFAB7","json":"https://pith.science/pith/KG37JDUH3Q7TBF6PVJIHVGFAB7.json","graph_json":"https://pith.science/api/pith-number/KG37JDUH3Q7TBF6PVJIHVGFAB7/graph.json","events_json":"https://pith.science/api/pith-number/KG37JDUH3Q7TBF6PVJIHVGFAB7/events.json","paper":"https://pith.science/paper/KG37JDUH"},"agent_actions":{"view_html":"https://pith.science/pith/KG37JDUH3Q7TBF6PVJIHVGFAB7","download_json":"https://pith.science/pith/KG37JDUH3Q7TBF6PVJIHVGFAB7.json","view_paper":"https://pith.science/paper/KG37JDUH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.07146&json=true","fetch_graph":"https://pith.science/api/pith-number/KG37JDUH3Q7TBF6PVJIHVGFAB7/graph.json","fetch_events":"https://pith.science/api/pith-number/KG37JDUH3Q7TBF6PVJIHVGFAB7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KG37JDUH3Q7TBF6PVJIHVGFAB7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KG37JDUH3Q7TBF6PVJIHVGFAB7/action/storage_attestation","attest_author":"https://pith.science/pith/KG37JDUH3Q7TBF6PVJIHVGFAB7/action/author_attestation","sign_citation":"https://pith.science/pith/KG37JDUH3Q7TBF6PVJIHVGFAB7/action/citation_signature","submit_replication":"https://pith.science/pith/KG37JDUH3Q7TBF6PVJIHVGFAB7/action/replication_record"}},"created_at":"2026-05-18T01:36:17.103515+00:00","updated_at":"2026-05-18T01:36:17.103515+00:00"}