{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:HMI7XTU7E3GAQIVLC6GR4E4GI3","short_pith_number":"pith:HMI7XTU7","schema_version":"1.0","canonical_sha256":"3b11fbce9f26cc0822ab178d1e138646db3dcd1bfff63de46678ba1c7d35e077","source":{"kind":"arxiv","id":"1901.09632","version":1},"attestation_state":"computed","paper":{"title":"Neural eliminators and classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Rafa{\\l} Adamczak, W{\\l}odzis{\\l}aw Duch, Yoichi Hayashi","submitted_at":"2019-01-28T12:57:32Z","abstract_excerpt":"Classification may not be reliable for several reasons: noise in the data, insufficient input information, overlapping distributions and sharp definition of classes. Faced with several possibilities neural network may in such cases still be useful if instead of a classification elimination of improbable classes is done. Eliminators may be constructed using classifiers assigning new cases to a pool of several classes instead of just one winning class. Elimination may be done with the help of several classifiers using modified error functions. A real life medical application of neural network is"},"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":"1901.09632","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-28T12:57:32Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"bab20a9a9a7ef597b1574eb356c745cb5a400bd096a565f49ae85b6626b6a3a8","abstract_canon_sha256":"edd8e9d422e52c4375e9e8991dfa73e2bd725215b3bf9c393f5699d5d5944913"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:24.265213Z","signature_b64":"pdKUVWnsl6HBfo6IP8PWpS4zl5P/lXtLa1S3A4DS2dPQKdFjRW8wCQUtvuF7czAU44vSUDZuyG/W0rrI5F1ABg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b11fbce9f26cc0822ab178d1e138646db3dcd1bfff63de46678ba1c7d35e077","last_reissued_at":"2026-05-17T23:55:24.264670Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:24.264670Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural eliminators and classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Rafa{\\l} Adamczak, W{\\l}odzis{\\l}aw Duch, Yoichi Hayashi","submitted_at":"2019-01-28T12:57:32Z","abstract_excerpt":"Classification may not be reliable for several reasons: noise in the data, insufficient input information, overlapping distributions and sharp definition of classes. Faced with several possibilities neural network may in such cases still be useful if instead of a classification elimination of improbable classes is done. Eliminators may be constructed using classifiers assigning new cases to a pool of several classes instead of just one winning class. Elimination may be done with the help of several classifiers using modified error functions. A real life medical application of neural network is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.09632","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":"1901.09632","created_at":"2026-05-17T23:55:24.264750+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.09632v1","created_at":"2026-05-17T23:55:24.264750+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.09632","created_at":"2026-05-17T23:55:24.264750+00:00"},{"alias_kind":"pith_short_12","alias_value":"HMI7XTU7E3GA","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"HMI7XTU7E3GAQIVL","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"HMI7XTU7","created_at":"2026-05-18T12:33:18.533446+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/HMI7XTU7E3GAQIVLC6GR4E4GI3","json":"https://pith.science/pith/HMI7XTU7E3GAQIVLC6GR4E4GI3.json","graph_json":"https://pith.science/api/pith-number/HMI7XTU7E3GAQIVLC6GR4E4GI3/graph.json","events_json":"https://pith.science/api/pith-number/HMI7XTU7E3GAQIVLC6GR4E4GI3/events.json","paper":"https://pith.science/paper/HMI7XTU7"},"agent_actions":{"view_html":"https://pith.science/pith/HMI7XTU7E3GAQIVLC6GR4E4GI3","download_json":"https://pith.science/pith/HMI7XTU7E3GAQIVLC6GR4E4GI3.json","view_paper":"https://pith.science/paper/HMI7XTU7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.09632&json=true","fetch_graph":"https://pith.science/api/pith-number/HMI7XTU7E3GAQIVLC6GR4E4GI3/graph.json","fetch_events":"https://pith.science/api/pith-number/HMI7XTU7E3GAQIVLC6GR4E4GI3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HMI7XTU7E3GAQIVLC6GR4E4GI3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HMI7XTU7E3GAQIVLC6GR4E4GI3/action/storage_attestation","attest_author":"https://pith.science/pith/HMI7XTU7E3GAQIVLC6GR4E4GI3/action/author_attestation","sign_citation":"https://pith.science/pith/HMI7XTU7E3GAQIVLC6GR4E4GI3/action/citation_signature","submit_replication":"https://pith.science/pith/HMI7XTU7E3GAQIVLC6GR4E4GI3/action/replication_record"}},"created_at":"2026-05-17T23:55:24.264750+00:00","updated_at":"2026-05-17T23:55:24.264750+00:00"}