{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:XSAT4X2TZND7ZCVS3J6DAQ3RFH","short_pith_number":"pith:XSAT4X2T","schema_version":"1.0","canonical_sha256":"bc813e5f53cb47fc8ab2da7c30437129eb07be324549dbe4c911b78ada36f502","source":{"kind":"arxiv","id":"1602.07865","version":1},"attestation_state":"computed","paper":{"title":"Projected Estimators for Robust Semi-supervised Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jesse H. Krijthe, Marco Loog","submitted_at":"2016-02-25T09:57:42Z","abstract_excerpt":"For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Using a projection of the supervised estimate onto a set of constraints imposed by the unlabeled data, we find we can safely improve over the supervised solution in terms of this quadratic loss. Unlike other approaches to semi-supervised learning, the procedure does not rely on assumptions that are not intrinsic to the classifier at hand. It is theoretically demonst"},"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":"1602.07865","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-25T09:57:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6a27781f6224e19ed08fc22de3fc38e33647a6d03bcd4e24e38da0906500c042","abstract_canon_sha256":"9bed5cb68150dd079c94f625a18f762cb8b3cd99e1cb6915b60706b0eca18fe8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:58.392080Z","signature_b64":"cOWSAGIfjmaQqyq/O0TLOE1o0TpmOgMC4FFEcV7LalmYMuRyDH6TIYYckl3ZEXKfoJCEDPGZkRdVi1rni9iICw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc813e5f53cb47fc8ab2da7c30437129eb07be324549dbe4c911b78ada36f502","last_reissued_at":"2026-05-18T01:19:58.391440Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:58.391440Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Projected Estimators for Robust Semi-supervised Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jesse H. Krijthe, Marco Loog","submitted_at":"2016-02-25T09:57:42Z","abstract_excerpt":"For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Using a projection of the supervised estimate onto a set of constraints imposed by the unlabeled data, we find we can safely improve over the supervised solution in terms of this quadratic loss. Unlike other approaches to semi-supervised learning, the procedure does not rely on assumptions that are not intrinsic to the classifier at hand. It is theoretically demonst"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.07865","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":"1602.07865","created_at":"2026-05-18T01:19:58.391524+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.07865v1","created_at":"2026-05-18T01:19:58.391524+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.07865","created_at":"2026-05-18T01:19:58.391524+00:00"},{"alias_kind":"pith_short_12","alias_value":"XSAT4X2TZND7","created_at":"2026-05-18T12:30:51.357362+00:00"},{"alias_kind":"pith_short_16","alias_value":"XSAT4X2TZND7ZCVS","created_at":"2026-05-18T12:30:51.357362+00:00"},{"alias_kind":"pith_short_8","alias_value":"XSAT4X2T","created_at":"2026-05-18T12:30:51.357362+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/XSAT4X2TZND7ZCVS3J6DAQ3RFH","json":"https://pith.science/pith/XSAT4X2TZND7ZCVS3J6DAQ3RFH.json","graph_json":"https://pith.science/api/pith-number/XSAT4X2TZND7ZCVS3J6DAQ3RFH/graph.json","events_json":"https://pith.science/api/pith-number/XSAT4X2TZND7ZCVS3J6DAQ3RFH/events.json","paper":"https://pith.science/paper/XSAT4X2T"},"agent_actions":{"view_html":"https://pith.science/pith/XSAT4X2TZND7ZCVS3J6DAQ3RFH","download_json":"https://pith.science/pith/XSAT4X2TZND7ZCVS3J6DAQ3RFH.json","view_paper":"https://pith.science/paper/XSAT4X2T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.07865&json=true","fetch_graph":"https://pith.science/api/pith-number/XSAT4X2TZND7ZCVS3J6DAQ3RFH/graph.json","fetch_events":"https://pith.science/api/pith-number/XSAT4X2TZND7ZCVS3J6DAQ3RFH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XSAT4X2TZND7ZCVS3J6DAQ3RFH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XSAT4X2TZND7ZCVS3J6DAQ3RFH/action/storage_attestation","attest_author":"https://pith.science/pith/XSAT4X2TZND7ZCVS3J6DAQ3RFH/action/author_attestation","sign_citation":"https://pith.science/pith/XSAT4X2TZND7ZCVS3J6DAQ3RFH/action/citation_signature","submit_replication":"https://pith.science/pith/XSAT4X2TZND7ZCVS3J6DAQ3RFH/action/replication_record"}},"created_at":"2026-05-18T01:19:58.391524+00:00","updated_at":"2026-05-18T01:19:58.391524+00:00"}