{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:6DIW2ZRNL4ZWVFVRUWQO6NSINF","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":"5e94c5eb814907fdacaf8de06f1d6605123f9724a0e2af3adfd88f3d69643576","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-12-06T00:55:51Z","title_canon_sha256":"4a6b5ac8610f8e70a3100a5c0972a6a95dc80fe4bdaac48752a27faf9939c5c1"},"schema_version":"1.0","source":{"id":"1312.1743","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1312.1743","created_at":"2026-05-18T02:49:47Z"},{"alias_kind":"arxiv_version","alias_value":"1312.1743v2","created_at":"2026-05-18T02:49:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.1743","created_at":"2026-05-18T02:49:47Z"},{"alias_kind":"pith_short_12","alias_value":"6DIW2ZRNL4ZW","created_at":"2026-05-18T12:27:36Z"},{"alias_kind":"pith_short_16","alias_value":"6DIW2ZRNL4ZWVFVR","created_at":"2026-05-18T12:27:36Z"},{"alias_kind":"pith_short_8","alias_value":"6DIW2ZRN","created_at":"2026-05-18T12:27:36Z"}],"graph_snapshots":[{"event_id":"sha256:f9841329ffd6725ede949667855df6d30d894a6792e0e6be14e71c3f1e0dcd4f","target":"graph","created_at":"2026-05-18T02:49:47Z","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":"This manuscript describes a method for training linear SVMs (including binary SVMs, SVM regression, and structural SVMs) from large, out-of-core training datasets. Current strategies for large-scale learning fall into one of two camps; batch algorithms which solve the learning problem given a finite datasets, and online algorithms which can process out-of-core datasets. The former typically requires datasets small enough to fit in memory. The latter is often phrased as a stochastic optimization problem; such algorithms enjoy strong theoretical properties but often require manual tuned annealin","authors_text":"Deva Ramanan","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-12-06T00:55:51Z","title":"Dual coordinate solvers for large-scale structural SVMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.1743","kind":"arxiv","version":2},"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:eeef4c46b10b2387deeca4a4860b55d2ff577d11efb1849c23267cb506c796b8","target":"record","created_at":"2026-05-18T02:49:47Z","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":"5e94c5eb814907fdacaf8de06f1d6605123f9724a0e2af3adfd88f3d69643576","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-12-06T00:55:51Z","title_canon_sha256":"4a6b5ac8610f8e70a3100a5c0972a6a95dc80fe4bdaac48752a27faf9939c5c1"},"schema_version":"1.0","source":{"id":"1312.1743","kind":"arxiv","version":2}},"canonical_sha256":"f0d16d662d5f336a96b1a5a0ef36486966a2e64dd33b430260cf40d187179734","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f0d16d662d5f336a96b1a5a0ef36486966a2e64dd33b430260cf40d187179734","first_computed_at":"2026-05-18T02:49:47.073335Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:49:47.073335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"orBBmBYMEGJ18eW4ewml2t9pT6Y6u75XzYYFA+9YtwK+RdfQ4EeEHlUASh47FoAKA4kQrbjRE1CuMvoqsKzqDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:49:47.073854Z","signed_message":"canonical_sha256_bytes"},"source_id":"1312.1743","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eeef4c46b10b2387deeca4a4860b55d2ff577d11efb1849c23267cb506c796b8","sha256:f9841329ffd6725ede949667855df6d30d894a6792e0e6be14e71c3f1e0dcd4f"],"state_sha256":"15ce0e5d8b7bb23bdf22e08ace068dbf3ac4f140fcd67fd145a63ce4de716b52"}