{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:VDTK3SLIPTXJLZHSFEBT73EZKV","short_pith_number":"pith:VDTK3SLI","schema_version":"1.0","canonical_sha256":"a8e6adc9687cee95e4f229033fec9955519c8844745b9a16884f0c0391fc2692","source":{"kind":"arxiv","id":"1611.07659","version":2},"attestation_state":"computed","paper":{"title":"Improving Efficiency of SVM k-fold Cross-validation by Alpha Seeding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bin Li, Jian Chen, Rao Kotagiri, Rui Zhang, Yawen Chen, Zeyi Wen","submitted_at":"2016-11-23T06:48:25Z","abstract_excerpt":"The k-fold cross-validation is commonly used to evaluate the effectiveness of SVMs with the selected hyper-parameters. It is known that the SVM k-fold cross-validation is expensive, since it requires training k SVMs. However, little work has explored reusing the h-th SVM for training the (h+1)-th SVM for improving the efficiency of k-fold cross-validation. In this paper, we propose three algorithms that reuse the h-th SVM for improving the efficiency of training the (h+1)-th SVM. Our key idea is to efficiently identify the support vectors and to accurately estimate their associated weights (al"},"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":"1611.07659","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-23T06:48:25Z","cross_cats_sorted":[],"title_canon_sha256":"e65159366788fa7cd70a0193e95dfd71480b79c9618ed114097a4d08be94ebe3","abstract_canon_sha256":"652dd148c9aa60a68e15b14d207b8a168295bac103b987bbdee17267603875e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:51:24.983533Z","signature_b64":"Jpa42CL4ILlbnN89Ueg3H3T3XftB0HI+jOtU+pO/ZCZ1jpF7cuIVAL0uqviQbuFy0tIePVJliDkE/cUo0r/SDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8e6adc9687cee95e4f229033fec9955519c8844745b9a16884f0c0391fc2692","last_reissued_at":"2026-05-18T00:51:24.982842Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:51:24.982842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Efficiency of SVM k-fold Cross-validation by Alpha Seeding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bin Li, Jian Chen, Rao Kotagiri, Rui Zhang, Yawen Chen, Zeyi Wen","submitted_at":"2016-11-23T06:48:25Z","abstract_excerpt":"The k-fold cross-validation is commonly used to evaluate the effectiveness of SVMs with the selected hyper-parameters. It is known that the SVM k-fold cross-validation is expensive, since it requires training k SVMs. However, little work has explored reusing the h-th SVM for training the (h+1)-th SVM for improving the efficiency of k-fold cross-validation. In this paper, we propose three algorithms that reuse the h-th SVM for improving the efficiency of training the (h+1)-th SVM. Our key idea is to efficiently identify the support vectors and to accurately estimate their associated weights (al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.07659","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1611.07659","created_at":"2026-05-18T00:51:24.982952+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.07659v2","created_at":"2026-05-18T00:51:24.982952+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.07659","created_at":"2026-05-18T00:51:24.982952+00:00"},{"alias_kind":"pith_short_12","alias_value":"VDTK3SLIPTXJ","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_16","alias_value":"VDTK3SLIPTXJLZHS","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_8","alias_value":"VDTK3SLI","created_at":"2026-05-18T12:30:48.956258+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/VDTK3SLIPTXJLZHSFEBT73EZKV","json":"https://pith.science/pith/VDTK3SLIPTXJLZHSFEBT73EZKV.json","graph_json":"https://pith.science/api/pith-number/VDTK3SLIPTXJLZHSFEBT73EZKV/graph.json","events_json":"https://pith.science/api/pith-number/VDTK3SLIPTXJLZHSFEBT73EZKV/events.json","paper":"https://pith.science/paper/VDTK3SLI"},"agent_actions":{"view_html":"https://pith.science/pith/VDTK3SLIPTXJLZHSFEBT73EZKV","download_json":"https://pith.science/pith/VDTK3SLIPTXJLZHSFEBT73EZKV.json","view_paper":"https://pith.science/paper/VDTK3SLI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.07659&json=true","fetch_graph":"https://pith.science/api/pith-number/VDTK3SLIPTXJLZHSFEBT73EZKV/graph.json","fetch_events":"https://pith.science/api/pith-number/VDTK3SLIPTXJLZHSFEBT73EZKV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VDTK3SLIPTXJLZHSFEBT73EZKV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VDTK3SLIPTXJLZHSFEBT73EZKV/action/storage_attestation","attest_author":"https://pith.science/pith/VDTK3SLIPTXJLZHSFEBT73EZKV/action/author_attestation","sign_citation":"https://pith.science/pith/VDTK3SLIPTXJLZHSFEBT73EZKV/action/citation_signature","submit_replication":"https://pith.science/pith/VDTK3SLIPTXJLZHSFEBT73EZKV/action/replication_record"}},"created_at":"2026-05-18T00:51:24.982952+00:00","updated_at":"2026-05-18T00:51:24.982952+00:00"}