{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:MPIUFMGA64FOAEZGRAGB6JZPQU","short_pith_number":"pith:MPIUFMGA","schema_version":"1.0","canonical_sha256":"63d142b0c0f70ae01326880c1f272f85251bf86ee53d9a355799d71f9b37171a","source":{"kind":"arxiv","id":"1605.04337","version":1},"attestation_state":"computed","paper":{"title":"Support Vector Algorithms for Optimizing the Partial Area Under the ROC Curve","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Harikrishna Narasimhan, Shivani Agarwal","submitted_at":"2016-05-13T21:33:45Z","abstract_excerpt":"The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve, but in terms of the \\emph{partial} area under the ROC curve between two false positive rates. In this paper, we develop support vector algorithms for directly optimizing the partial AUC between any two false positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. "},"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":"1605.04337","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-13T21:33:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"709a9e55b9d035492bd90d99e3631c7757b02e698ef84b89bd78bfb396b76580","abstract_canon_sha256":"4a01d559bf713ed6e7bd49eaffbef7e8477fc13a846559a1394253b9b66c7c92"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:56:31.069152Z","signature_b64":"OWOZ9U0kxctu+/PKoEfr77+t7sn2JoMEz2C4kRpRSkCzz+Zh2NEqY4AvtdARlk82kKrOrCCmGKmOsvuggAUnBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63d142b0c0f70ae01326880c1f272f85251bf86ee53d9a355799d71f9b37171a","last_reissued_at":"2026-05-18T00:56:31.068448Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:56:31.068448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Support Vector Algorithms for Optimizing the Partial Area Under the ROC Curve","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Harikrishna Narasimhan, Shivani Agarwal","submitted_at":"2016-05-13T21:33:45Z","abstract_excerpt":"The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve, but in terms of the \\emph{partial} area under the ROC curve between two false positive rates. In this paper, we develop support vector algorithms for directly optimizing the partial AUC between any two false positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.04337","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":"1605.04337","created_at":"2026-05-18T00:56:31.068565+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.04337v1","created_at":"2026-05-18T00:56:31.068565+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.04337","created_at":"2026-05-18T00:56:31.068565+00:00"},{"alias_kind":"pith_short_12","alias_value":"MPIUFMGA64FO","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"MPIUFMGA64FOAEZG","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"MPIUFMGA","created_at":"2026-05-18T12:30:32.724797+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/MPIUFMGA64FOAEZGRAGB6JZPQU","json":"https://pith.science/pith/MPIUFMGA64FOAEZGRAGB6JZPQU.json","graph_json":"https://pith.science/api/pith-number/MPIUFMGA64FOAEZGRAGB6JZPQU/graph.json","events_json":"https://pith.science/api/pith-number/MPIUFMGA64FOAEZGRAGB6JZPQU/events.json","paper":"https://pith.science/paper/MPIUFMGA"},"agent_actions":{"view_html":"https://pith.science/pith/MPIUFMGA64FOAEZGRAGB6JZPQU","download_json":"https://pith.science/pith/MPIUFMGA64FOAEZGRAGB6JZPQU.json","view_paper":"https://pith.science/paper/MPIUFMGA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.04337&json=true","fetch_graph":"https://pith.science/api/pith-number/MPIUFMGA64FOAEZGRAGB6JZPQU/graph.json","fetch_events":"https://pith.science/api/pith-number/MPIUFMGA64FOAEZGRAGB6JZPQU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MPIUFMGA64FOAEZGRAGB6JZPQU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MPIUFMGA64FOAEZGRAGB6JZPQU/action/storage_attestation","attest_author":"https://pith.science/pith/MPIUFMGA64FOAEZGRAGB6JZPQU/action/author_attestation","sign_citation":"https://pith.science/pith/MPIUFMGA64FOAEZGRAGB6JZPQU/action/citation_signature","submit_replication":"https://pith.science/pith/MPIUFMGA64FOAEZGRAGB6JZPQU/action/replication_record"}},"created_at":"2026-05-18T00:56:31.068565+00:00","updated_at":"2026-05-18T00:56:31.068565+00:00"}