{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:CQKBQRKXP3EXSDPB2ZBMNX25M5","short_pith_number":"pith:CQKBQRKX","schema_version":"1.0","canonical_sha256":"14141845577ec9790de1d642c6df5d676a948866033606a89d4187011efbc6ab","source":{"kind":"arxiv","id":"2001.10642","version":1},"attestation_state":"computed","paper":{"title":"Binary Classification from Positive Data with Skewed Confidence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hirotaka Kaji, Kazuhiko Shinoda, Masashi Sugiyama","submitted_at":"2020-01-29T00:04:36Z","abstract_excerpt":"Positive-confidence (Pconf) classification [Ishida et al., 2018] is a promising weakly-supervised learning method which trains a binary classifier only from positive data equipped with confidence. However, in practice, the confidence may be skewed by bias arising in an annotation process. The Pconf classifier cannot be properly learned with skewed confidence, and consequently, the classification performance might be deteriorated. In this paper, we introduce the parameterized model of the skewed confidence, and propose the method for selecting the hyperparameter which cancels out the negative i"},"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":"2001.10642","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-01-29T00:04:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1df04cf272d3df3028097ec34fd01a3fdc78325b9f58fb14454a84e7491a0a83","abstract_canon_sha256":"18960c456546499e9ab67e37fc169e2bc32d83808381cf7e1be74478f6ecb6bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:37:11.006743Z","signature_b64":"bpQxAmPYO4bMJYwgJ1cuGI3nLa/mrhlebYzXEmNqVBLIcN5D4Mk+0mk+Qv0Ych9bIXeWA6EH7eSWBjKfXpDOBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14141845577ec9790de1d642c6df5d676a948866033606a89d4187011efbc6ab","last_reissued_at":"2026-07-05T00:37:11.006381Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:37:11.006381Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Binary Classification from Positive Data with Skewed Confidence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hirotaka Kaji, Kazuhiko Shinoda, Masashi Sugiyama","submitted_at":"2020-01-29T00:04:36Z","abstract_excerpt":"Positive-confidence (Pconf) classification [Ishida et al., 2018] is a promising weakly-supervised learning method which trains a binary classifier only from positive data equipped with confidence. However, in practice, the confidence may be skewed by bias arising in an annotation process. The Pconf classifier cannot be properly learned with skewed confidence, and consequently, the classification performance might be deteriorated. In this paper, we introduce the parameterized model of the skewed confidence, and propose the method for selecting the hyperparameter which cancels out the negative i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.10642","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/2001.10642/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":"2001.10642","created_at":"2026-07-05T00:37:11.006446+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.10642v1","created_at":"2026-07-05T00:37:11.006446+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.10642","created_at":"2026-07-05T00:37:11.006446+00:00"},{"alias_kind":"pith_short_12","alias_value":"CQKBQRKXP3EX","created_at":"2026-07-05T00:37:11.006446+00:00"},{"alias_kind":"pith_short_16","alias_value":"CQKBQRKXP3EXSDPB","created_at":"2026-07-05T00:37:11.006446+00:00"},{"alias_kind":"pith_short_8","alias_value":"CQKBQRKX","created_at":"2026-07-05T00:37:11.006446+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/CQKBQRKXP3EXSDPB2ZBMNX25M5","json":"https://pith.science/pith/CQKBQRKXP3EXSDPB2ZBMNX25M5.json","graph_json":"https://pith.science/api/pith-number/CQKBQRKXP3EXSDPB2ZBMNX25M5/graph.json","events_json":"https://pith.science/api/pith-number/CQKBQRKXP3EXSDPB2ZBMNX25M5/events.json","paper":"https://pith.science/paper/CQKBQRKX"},"agent_actions":{"view_html":"https://pith.science/pith/CQKBQRKXP3EXSDPB2ZBMNX25M5","download_json":"https://pith.science/pith/CQKBQRKXP3EXSDPB2ZBMNX25M5.json","view_paper":"https://pith.science/paper/CQKBQRKX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.10642&json=true","fetch_graph":"https://pith.science/api/pith-number/CQKBQRKXP3EXSDPB2ZBMNX25M5/graph.json","fetch_events":"https://pith.science/api/pith-number/CQKBQRKXP3EXSDPB2ZBMNX25M5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CQKBQRKXP3EXSDPB2ZBMNX25M5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CQKBQRKXP3EXSDPB2ZBMNX25M5/action/storage_attestation","attest_author":"https://pith.science/pith/CQKBQRKXP3EXSDPB2ZBMNX25M5/action/author_attestation","sign_citation":"https://pith.science/pith/CQKBQRKXP3EXSDPB2ZBMNX25M5/action/citation_signature","submit_replication":"https://pith.science/pith/CQKBQRKXP3EXSDPB2ZBMNX25M5/action/replication_record"}},"created_at":"2026-07-05T00:37:11.006446+00:00","updated_at":"2026-07-05T00:37:11.006446+00:00"}