{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:Q6HL2NEFIGB4HQYRE4CF57GP42","short_pith_number":"pith:Q6HL2NEF","schema_version":"1.0","canonical_sha256":"878ebd34854183c3c31127045efccfe692ddecde3db61c43fcc510034051466a","source":{"kind":"arxiv","id":"1210.0645","version":5},"attestation_state":"computed","paper":{"title":"Nonparametric Unsupervised Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Thomas S. Huang, Yingzhen Yang","submitted_at":"2012-10-02T04:22:50Z","abstract_excerpt":"Unsupervised classification methods learn a discriminative classifier from unlabeled data, which has been proven to be an effective way of simultaneously clustering the data and training a classifier from the data. Various unsupervised classification methods obtain appealing results by the classifiers learned in an unsupervised manner. However, existing methods do not consider the misclassification error of the unsupervised classifiers except unsupervised SVM, so the performance of the unsupervised classifiers is not fully evaluated. In this work, we study the misclassification error of two po"},"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":"1210.0645","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-02T04:22:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0135178566413e345fe40beafcc65a6f0503375e54a4f7b761205c7d61132698","abstract_canon_sha256":"cd3ff8fac362f36984ac391974353337fcbd0364d45a22c34feed71b3e2559b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:25:14.515340Z","signature_b64":"gYYfienubTj004ZKeFHkCfetisELnJMMh56gByamz5nEVrZEs9rr5sy6HzIOWclkPBzNfZjxbpYsUdDpwwbFBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"878ebd34854183c3c31127045efccfe692ddecde3db61c43fcc510034051466a","last_reissued_at":"2026-05-18T03:25:14.514526Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:25:14.514526Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric Unsupervised Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Thomas S. Huang, Yingzhen Yang","submitted_at":"2012-10-02T04:22:50Z","abstract_excerpt":"Unsupervised classification methods learn a discriminative classifier from unlabeled data, which has been proven to be an effective way of simultaneously clustering the data and training a classifier from the data. Various unsupervised classification methods obtain appealing results by the classifiers learned in an unsupervised manner. However, existing methods do not consider the misclassification error of the unsupervised classifiers except unsupervised SVM, so the performance of the unsupervised classifiers is not fully evaluated. In this work, we study the misclassification error of two po"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.0645","kind":"arxiv","version":5},"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":"1210.0645","created_at":"2026-05-18T03:25:14.514652+00:00"},{"alias_kind":"arxiv_version","alias_value":"1210.0645v5","created_at":"2026-05-18T03:25:14.514652+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1210.0645","created_at":"2026-05-18T03:25:14.514652+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q6HL2NEFIGB4","created_at":"2026-05-18T12:27:18.751474+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q6HL2NEFIGB4HQYR","created_at":"2026-05-18T12:27:18.751474+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q6HL2NEF","created_at":"2026-05-18T12:27:18.751474+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/Q6HL2NEFIGB4HQYRE4CF57GP42","json":"https://pith.science/pith/Q6HL2NEFIGB4HQYRE4CF57GP42.json","graph_json":"https://pith.science/api/pith-number/Q6HL2NEFIGB4HQYRE4CF57GP42/graph.json","events_json":"https://pith.science/api/pith-number/Q6HL2NEFIGB4HQYRE4CF57GP42/events.json","paper":"https://pith.science/paper/Q6HL2NEF"},"agent_actions":{"view_html":"https://pith.science/pith/Q6HL2NEFIGB4HQYRE4CF57GP42","download_json":"https://pith.science/pith/Q6HL2NEFIGB4HQYRE4CF57GP42.json","view_paper":"https://pith.science/paper/Q6HL2NEF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1210.0645&json=true","fetch_graph":"https://pith.science/api/pith-number/Q6HL2NEFIGB4HQYRE4CF57GP42/graph.json","fetch_events":"https://pith.science/api/pith-number/Q6HL2NEFIGB4HQYRE4CF57GP42/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q6HL2NEFIGB4HQYRE4CF57GP42/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q6HL2NEFIGB4HQYRE4CF57GP42/action/storage_attestation","attest_author":"https://pith.science/pith/Q6HL2NEFIGB4HQYRE4CF57GP42/action/author_attestation","sign_citation":"https://pith.science/pith/Q6HL2NEFIGB4HQYRE4CF57GP42/action/citation_signature","submit_replication":"https://pith.science/pith/Q6HL2NEFIGB4HQYRE4CF57GP42/action/replication_record"}},"created_at":"2026-05-18T03:25:14.514652+00:00","updated_at":"2026-05-18T03:25:14.514652+00:00"}