{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:KZAUMGHZJ5O42WMATPU5NR4NYE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c46210fb513112b88c4ed19bf734231424ae3b6618686215286626c322dc1e60","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-06-22T00:26:59Z","title_canon_sha256":"f53fd14357bee16f8241163487d3c2535906f9209f741d2cdd02f4aa34fe65f7"},"schema_version":"1.0","source":{"id":"1606.06793","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.06793","created_at":"2026-05-18T00:46:55Z"},{"alias_kind":"arxiv_version","alias_value":"1606.06793v3","created_at":"2026-05-18T00:46:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.06793","created_at":"2026-05-18T00:46:55Z"},{"alias_kind":"pith_short_12","alias_value":"KZAUMGHZJ5O4","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"KZAUMGHZJ5O42WMA","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"KZAUMGHZ","created_at":"2026-05-18T12:30:29Z"}],"graph_snapshots":[{"event_id":"sha256:edd45565b64350ab0ba230a20613ce1e92bfc010baa6e80f3899bfddbc45a7ab","target":"graph","created_at":"2026-05-18T00:46:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Acquiring labels are often costly, whereas unlabeled data are usually easy to obtain in modern machine learning applications. Semi-supervised learning provides a principled machine learning framework to address such situations, and has been applied successfully in many real-word applications and industries. Nonetheless, most of existing semi-supervised learning methods encounter two serious limitations when applied to modern and large-scale datasets: computational burden and memory usage demand. To this end, we present in this paper the Graph-based semi-supervised Kernel Machine (GKM), a metho","authors_text":"Dinh Phung, Khanh Nguyen, Trung Le, Van Nguyen, Vu Nguyen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-06-22T00:26:59Z","title":"Scalable Semi-supervised Learning with Graph-based Kernel Machine"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.06793","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8c3becc163bf66956b81f61c50330b4969b000f10656a5dc2063b53bcd1f29a0","target":"record","created_at":"2026-05-18T00:46:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c46210fb513112b88c4ed19bf734231424ae3b6618686215286626c322dc1e60","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-06-22T00:26:59Z","title_canon_sha256":"f53fd14357bee16f8241163487d3c2535906f9209f741d2cdd02f4aa34fe65f7"},"schema_version":"1.0","source":{"id":"1606.06793","kind":"arxiv","version":3}},"canonical_sha256":"56414618f94f5dcd59809be9d6c78dc100ed8166d472d606fd573165b8f84839","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"56414618f94f5dcd59809be9d6c78dc100ed8166d472d606fd573165b8f84839","first_computed_at":"2026-05-18T00:46:55.678005Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:46:55.678005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bqMwlElPsQRp+Fe4UpN/y7uW9RcHmFu3dEf6fTzxNK7gMAXsbqCIPZPAdjLAAxPvKLh7xBr8VZv1P9gPu3LSCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:46:55.678476Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.06793","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8c3becc163bf66956b81f61c50330b4969b000f10656a5dc2063b53bcd1f29a0","sha256:edd45565b64350ab0ba230a20613ce1e92bfc010baa6e80f3899bfddbc45a7ab"],"state_sha256":"432f823febe77f175b45e60db30f391f3fb03860d5a7075c054ca3d26747b3d1"}