{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:HDXHTLUTV4PTO33XIV2U4EM2MR","short_pith_number":"pith:HDXHTLUT","schema_version":"1.0","canonical_sha256":"38ee79ae93af1f376f7745754e119a6441e31dd5b0b8aef4af7109c74097241f","source":{"kind":"arxiv","id":"2005.13120","version":2},"attestation_state":"computed","paper":{"title":"Data Separability for Neural Network Classifiers and the Development of a Separability Index","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hanseok Ko, Murray Loew, Shuyue Guan","submitted_at":"2020-05-27T01:49:19Z","abstract_excerpt":"In machine learning, the performance of a classifier depends on both the classifier model and the dataset. For a specific neural network classifier, the training process varies with the training set used; some training data make training accuracy fast converged to high values, while some data may lead to slowly converged to lower accuracy. To quantify this phenomenon, we created the Distance-based Separability Index (DSI), which is independent of the classifier model, to measure the separability of datasets. In this paper, we consider the situation where different classes of data are mixed tog"},"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":"2005.13120","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-27T01:49:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5fa893d96c40271fd1fa2497dd66d2fc05c2eac960d64185eadd17c328dd0ad1","abstract_canon_sha256":"0af4fca8ed2ee95912dda17394a23a683c27e6f56dab893ad60164f19ecca3a4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:06:34.804066Z","signature_b64":"fItVpx84ozyWEwKz0zJM+I00GKbaEQrnhWMzjmiNSgiwhchInCkJqshz8dV3b2tLaQw0brmTH1ypWuk/x8lmBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38ee79ae93af1f376f7745754e119a6441e31dd5b0b8aef4af7109c74097241f","last_reissued_at":"2026-07-05T01:06:34.803703Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:06:34.803703Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data Separability for Neural Network Classifiers and the Development of a Separability Index","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hanseok Ko, Murray Loew, Shuyue Guan","submitted_at":"2020-05-27T01:49:19Z","abstract_excerpt":"In machine learning, the performance of a classifier depends on both the classifier model and the dataset. For a specific neural network classifier, the training process varies with the training set used; some training data make training accuracy fast converged to high values, while some data may lead to slowly converged to lower accuracy. To quantify this phenomenon, we created the Distance-based Separability Index (DSI), which is independent of the classifier model, to measure the separability of datasets. In this paper, we consider the situation where different classes of data are mixed tog"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.13120","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.13120/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":"2005.13120","created_at":"2026-07-05T01:06:34.803755+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.13120v2","created_at":"2026-07-05T01:06:34.803755+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.13120","created_at":"2026-07-05T01:06:34.803755+00:00"},{"alias_kind":"pith_short_12","alias_value":"HDXHTLUTV4PT","created_at":"2026-07-05T01:06:34.803755+00:00"},{"alias_kind":"pith_short_16","alias_value":"HDXHTLUTV4PTO33X","created_at":"2026-07-05T01:06:34.803755+00:00"},{"alias_kind":"pith_short_8","alias_value":"HDXHTLUT","created_at":"2026-07-05T01:06:34.803755+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/HDXHTLUTV4PTO33XIV2U4EM2MR","json":"https://pith.science/pith/HDXHTLUTV4PTO33XIV2U4EM2MR.json","graph_json":"https://pith.science/api/pith-number/HDXHTLUTV4PTO33XIV2U4EM2MR/graph.json","events_json":"https://pith.science/api/pith-number/HDXHTLUTV4PTO33XIV2U4EM2MR/events.json","paper":"https://pith.science/paper/HDXHTLUT"},"agent_actions":{"view_html":"https://pith.science/pith/HDXHTLUTV4PTO33XIV2U4EM2MR","download_json":"https://pith.science/pith/HDXHTLUTV4PTO33XIV2U4EM2MR.json","view_paper":"https://pith.science/paper/HDXHTLUT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.13120&json=true","fetch_graph":"https://pith.science/api/pith-number/HDXHTLUTV4PTO33XIV2U4EM2MR/graph.json","fetch_events":"https://pith.science/api/pith-number/HDXHTLUTV4PTO33XIV2U4EM2MR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HDXHTLUTV4PTO33XIV2U4EM2MR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HDXHTLUTV4PTO33XIV2U4EM2MR/action/storage_attestation","attest_author":"https://pith.science/pith/HDXHTLUTV4PTO33XIV2U4EM2MR/action/author_attestation","sign_citation":"https://pith.science/pith/HDXHTLUTV4PTO33XIV2U4EM2MR/action/citation_signature","submit_replication":"https://pith.science/pith/HDXHTLUTV4PTO33XIV2U4EM2MR/action/replication_record"}},"created_at":"2026-07-05T01:06:34.803755+00:00","updated_at":"2026-07-05T01:06:34.803755+00:00"}