{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NUSG6VGTOG76IBUH2355OQBVLE","short_pith_number":"pith:NUSG6VGT","schema_version":"1.0","canonical_sha256":"6d246f54d371bfe40687d6fbd7403559236fe023fe69c294a21dc3f976e0ab59","source":{"kind":"arxiv","id":"2403.01769","version":1},"attestation_state":"computed","paper":{"title":"A Safe Screening Rule with Bi-level Optimization of $\\nu$ Support Vector Machine","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","math.OC"],"primary_cat":"cs.LG","authors_text":"Huan Zhang, Jianhua Zhao, Lei Shi, Wanyi Chen, Yitian Xu, Zhiji Yang","submitted_at":"2024-03-04T06:55:57Z","abstract_excerpt":"Support vector machine (SVM) has achieved many successes in machine learning, especially for a small sample problem. As a famous extension of the traditional SVM, the $\\nu$ support vector machine ($\\nu$-SVM) has shown outstanding performance due to its great model interpretability. However, it still faces challenges in training overhead for large-scale problems. To address this issue, we propose a safe screening rule with bi-level optimization for $\\nu$-SVM (SRBO-$\\nu$-SVM) which can screen out inactive samples before training and reduce the computational cost without sacrificing the predictio"},"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":"2403.01769","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-04T06:55:57Z","cross_cats_sorted":["cs.AI","math.OC"],"title_canon_sha256":"5f885586d11215d82d5d63df18fe213fa66c25082fcdf10ac6af9b8c3df1ccb2","abstract_canon_sha256":"3698f0ab1732a928cea37c16b46ba7720496e71df5188add3cd4e6b1a0513bba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:51:51.458554Z","signature_b64":"0Ih9hLNpq9nVNOhQpCUurtVxVN1niSHRUEDx1IC/EXTf2fNYsVsjIOsuI2GvGr+wrqLDU5274cb7Ffr9q2kJDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d246f54d371bfe40687d6fbd7403559236fe023fe69c294a21dc3f976e0ab59","last_reissued_at":"2026-07-05T07:51:51.458199Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:51:51.458199Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Safe Screening Rule with Bi-level Optimization of $\\nu$ Support Vector Machine","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","math.OC"],"primary_cat":"cs.LG","authors_text":"Huan Zhang, Jianhua Zhao, Lei Shi, Wanyi Chen, Yitian Xu, Zhiji Yang","submitted_at":"2024-03-04T06:55:57Z","abstract_excerpt":"Support vector machine (SVM) has achieved many successes in machine learning, especially for a small sample problem. As a famous extension of the traditional SVM, the $\\nu$ support vector machine ($\\nu$-SVM) has shown outstanding performance due to its great model interpretability. However, it still faces challenges in training overhead for large-scale problems. To address this issue, we propose a safe screening rule with bi-level optimization for $\\nu$-SVM (SRBO-$\\nu$-SVM) which can screen out inactive samples before training and reduce the computational cost without sacrificing the predictio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.01769","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.01769/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2403.01769","created_at":"2026-07-05T07:51:51.458255+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.01769v1","created_at":"2026-07-05T07:51:51.458255+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.01769","created_at":"2026-07-05T07:51:51.458255+00:00"},{"alias_kind":"pith_short_12","alias_value":"NUSG6VGTOG76","created_at":"2026-07-05T07:51:51.458255+00:00"},{"alias_kind":"pith_short_16","alias_value":"NUSG6VGTOG76IBUH","created_at":"2026-07-05T07:51:51.458255+00:00"},{"alias_kind":"pith_short_8","alias_value":"NUSG6VGT","created_at":"2026-07-05T07:51:51.458255+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/NUSG6VGTOG76IBUH2355OQBVLE","json":"https://pith.science/pith/NUSG6VGTOG76IBUH2355OQBVLE.json","graph_json":"https://pith.science/api/pith-number/NUSG6VGTOG76IBUH2355OQBVLE/graph.json","events_json":"https://pith.science/api/pith-number/NUSG6VGTOG76IBUH2355OQBVLE/events.json","paper":"https://pith.science/paper/NUSG6VGT"},"agent_actions":{"view_html":"https://pith.science/pith/NUSG6VGTOG76IBUH2355OQBVLE","download_json":"https://pith.science/pith/NUSG6VGTOG76IBUH2355OQBVLE.json","view_paper":"https://pith.science/paper/NUSG6VGT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.01769&json=true","fetch_graph":"https://pith.science/api/pith-number/NUSG6VGTOG76IBUH2355OQBVLE/graph.json","fetch_events":"https://pith.science/api/pith-number/NUSG6VGTOG76IBUH2355OQBVLE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NUSG6VGTOG76IBUH2355OQBVLE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NUSG6VGTOG76IBUH2355OQBVLE/action/storage_attestation","attest_author":"https://pith.science/pith/NUSG6VGTOG76IBUH2355OQBVLE/action/author_attestation","sign_citation":"https://pith.science/pith/NUSG6VGTOG76IBUH2355OQBVLE/action/citation_signature","submit_replication":"https://pith.science/pith/NUSG6VGTOG76IBUH2355OQBVLE/action/replication_record"}},"created_at":"2026-07-05T07:51:51.458255+00:00","updated_at":"2026-07-05T07:51:51.458255+00:00"}