{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:CY37KBEK3P56MCYIHHUD2QF3ZP","short_pith_number":"pith:CY37KBEK","canonical_record":{"source":{"id":"1306.3108","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-13T13:47:51Z","cross_cats_sorted":[],"title_canon_sha256":"b12c79bc0a3f002ac129e54ee5ae7870efacb6f95d31df96570aeedfaac89ce8","abstract_canon_sha256":"68bb5414df7faff20798d5e2e6ad712147ef32b70a432e2c7fafe184ae6e35d2"},"schema_version":"1.0"},"canonical_sha256":"1637f5048adbfbe60b0839e83d40bbcbea59dc10bed24749ef3473904b35b83b","source":{"kind":"arxiv","id":"1306.3108","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1306.3108","created_at":"2026-05-18T03:14:40Z"},{"alias_kind":"arxiv_version","alias_value":"1306.3108v2","created_at":"2026-05-18T03:14:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.3108","created_at":"2026-05-18T03:14:40Z"},{"alias_kind":"pith_short_12","alias_value":"CY37KBEK3P56","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_16","alias_value":"CY37KBEK3P56MCYI","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_8","alias_value":"CY37KBEK","created_at":"2026-05-18T12:27:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:CY37KBEK3P56MCYIHHUD2QF3ZP","target":"record","payload":{"canonical_record":{"source":{"id":"1306.3108","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-13T13:47:51Z","cross_cats_sorted":[],"title_canon_sha256":"b12c79bc0a3f002ac129e54ee5ae7870efacb6f95d31df96570aeedfaac89ce8","abstract_canon_sha256":"68bb5414df7faff20798d5e2e6ad712147ef32b70a432e2c7fafe184ae6e35d2"},"schema_version":"1.0"},"canonical_sha256":"1637f5048adbfbe60b0839e83d40bbcbea59dc10bed24749ef3473904b35b83b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:14:40.673776Z","signature_b64":"sn1GAwMzW6QYsr/7oE6kIOEMGeKK2Rx/9RXOHU0HsZnUZ9w6fxa/U7PmcBeFLKTtH8BW8uTFPpgk1EHjN3DPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1637f5048adbfbe60b0839e83d40bbcbea59dc10bed24749ef3473904b35b83b","last_reissued_at":"2026-05-18T03:14:40.673103Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:14:40.673103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1306.3108","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:14:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YgrckKV0QDROVyA51TQnLoJgn/MUxoKZI5VyDL7FTRsY2ZV3sd8rG8djn+81zG3OS8QjmRZ/Ry9m5QWye1nhBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T06:18:46.045767Z"},"content_sha256":"3443b159cf4bd40b3fd71e77d5d55f13d64202f638a8e5da88638b6ee8bd80e2","schema_version":"1.0","event_id":"sha256:3443b159cf4bd40b3fd71e77d5d55f13d64202f638a8e5da88638b6ee8bd80e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:CY37KBEK3P56MCYIHHUD2QF3ZP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Guaranteed Classification via Regularized Similarity Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Yiming Ying, Zheng-Chu Guo","submitted_at":"2013-06-13T13:47:51Z","abstract_excerpt":"Learning an appropriate (dis)similarity function from the available data is a central problem in machine learning, since the success of many machine learning algorithms critically depends on the choice of a similarity function to compare examples. Despite many approaches for similarity metric learning have been proposed, there is little theoretical study on the links between similarity met- ric learning and the classification performance of the result classifier. In this paper, we propose a regularized similarity learning formulation associated with general matrix-norms, and establish their ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.3108","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:14:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZTpV2YbW5xxTmQCuTLnw1+Y6j8YSbJCT3zdWJDdVyzcRDQAly39stzKG4pyAkk8sk4btat8u1OzRHiDsMBBeCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T06:18:46.046133Z"},"content_sha256":"ff7c0797c23e5a4e0d89b1e440f3a7a99d8de773aa65b0390d3e01682bac8a32","schema_version":"1.0","event_id":"sha256:ff7c0797c23e5a4e0d89b1e440f3a7a99d8de773aa65b0390d3e01682bac8a32"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CY37KBEK3P56MCYIHHUD2QF3ZP/bundle.json","state_url":"https://pith.science/pith/CY37KBEK3P56MCYIHHUD2QF3ZP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CY37KBEK3P56MCYIHHUD2QF3ZP/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T06:18:46Z","links":{"resolver":"https://pith.science/pith/CY37KBEK3P56MCYIHHUD2QF3ZP","bundle":"https://pith.science/pith/CY37KBEK3P56MCYIHHUD2QF3ZP/bundle.json","state":"https://pith.science/pith/CY37KBEK3P56MCYIHHUD2QF3ZP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CY37KBEK3P56MCYIHHUD2QF3ZP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:CY37KBEK3P56MCYIHHUD2QF3ZP","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":"68bb5414df7faff20798d5e2e6ad712147ef32b70a432e2c7fafe184ae6e35d2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-13T13:47:51Z","title_canon_sha256":"b12c79bc0a3f002ac129e54ee5ae7870efacb6f95d31df96570aeedfaac89ce8"},"schema_version":"1.0","source":{"id":"1306.3108","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1306.3108","created_at":"2026-05-18T03:14:40Z"},{"alias_kind":"arxiv_version","alias_value":"1306.3108v2","created_at":"2026-05-18T03:14:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.3108","created_at":"2026-05-18T03:14:40Z"},{"alias_kind":"pith_short_12","alias_value":"CY37KBEK3P56","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_16","alias_value":"CY37KBEK3P56MCYI","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_8","alias_value":"CY37KBEK","created_at":"2026-05-18T12:27:40Z"}],"graph_snapshots":[{"event_id":"sha256:ff7c0797c23e5a4e0d89b1e440f3a7a99d8de773aa65b0390d3e01682bac8a32","target":"graph","created_at":"2026-05-18T03:14:40Z","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":"Learning an appropriate (dis)similarity function from the available data is a central problem in machine learning, since the success of many machine learning algorithms critically depends on the choice of a similarity function to compare examples. Despite many approaches for similarity metric learning have been proposed, there is little theoretical study on the links between similarity met- ric learning and the classification performance of the result classifier. In this paper, we propose a regularized similarity learning formulation associated with general matrix-norms, and establish their ge","authors_text":"Yiming Ying, Zheng-Chu Guo","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-13T13:47:51Z","title":"Guaranteed Classification via Regularized Similarity Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.3108","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":"sha256:3443b159cf4bd40b3fd71e77d5d55f13d64202f638a8e5da88638b6ee8bd80e2","target":"record","created_at":"2026-05-18T03:14:40Z","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":"68bb5414df7faff20798d5e2e6ad712147ef32b70a432e2c7fafe184ae6e35d2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-13T13:47:51Z","title_canon_sha256":"b12c79bc0a3f002ac129e54ee5ae7870efacb6f95d31df96570aeedfaac89ce8"},"schema_version":"1.0","source":{"id":"1306.3108","kind":"arxiv","version":2}},"canonical_sha256":"1637f5048adbfbe60b0839e83d40bbcbea59dc10bed24749ef3473904b35b83b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1637f5048adbfbe60b0839e83d40bbcbea59dc10bed24749ef3473904b35b83b","first_computed_at":"2026-05-18T03:14:40.673103Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:14:40.673103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sn1GAwMzW6QYsr/7oE6kIOEMGeKK2Rx/9RXOHU0HsZnUZ9w6fxa/U7PmcBeFLKTtH8BW8uTFPpgk1EHjN3DPCA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:14:40.673776Z","signed_message":"canonical_sha256_bytes"},"source_id":"1306.3108","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3443b159cf4bd40b3fd71e77d5d55f13d64202f638a8e5da88638b6ee8bd80e2","sha256:ff7c0797c23e5a4e0d89b1e440f3a7a99d8de773aa65b0390d3e01682bac8a32"],"state_sha256":"19f3c949392bc23b0f2905bbe2d30fc0d2897919b21db9cc3564c14511ed23fa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oZD4Wk5dkwtxYEAt8cQdkAY8+IxSEFuc8uSKNUXx0Jx1e76L73WhDSZqc4E/X1VInnIIDSumQF2rq/qPB3/ODA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T06:18:46.048144Z","bundle_sha256":"0867a42c0a04488cc2b8aade73fdf8fe599a1c69170c7cbf97cfb42b90e1fed8"}}