{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:V2H5PLIK7G6PMEM7PRPPO3KIX5","short_pith_number":"pith:V2H5PLIK","schema_version":"1.0","canonical_sha256":"ae8fd7ad0af9bcf6119f7c5ef76d48bf6b13df5df311c938d0a99507a11e2046","source":{"kind":"arxiv","id":"1810.13333","version":2},"attestation_state":"computed","paper":{"title":"Boosting for Comparison-Based Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Micha\\\"el Perrot, Ulrike von Luxburg","submitted_at":"2018-10-31T15:26:12Z","abstract_excerpt":"We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form \"object $x_i$ is closer to object $x_j$ than to object $x_k$.\" In this paper we introduce TripletBoost, a new method that can learn a classifier just from such triplet comparisons. The main idea is to aggregate the triplets information into weak classifiers, which can subsequently be boosted to a strong classifier. Our method has two main advantages: (i) it is applicable to data from any metric space, and (ii) it can deal with large scale probl"},"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":"1810.13333","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-31T15:26:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6d35424acd26b7b2fc3dbe90d3e72ea23e767e553c5649516877743840ea05a0","abstract_canon_sha256":"a4ad7a8f5cf32ab6e8196ca0a4b3c9af5eb596c16faaba0f6b9b25cebc51fe84"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:47.947810Z","signature_b64":"w749u73lQz2by3+ldKPIBwxbEN4V9k1wbzC61kPZ9TyVgABVhJGt/O7amTxy61uWn1Lv/sAItcATJFQr5BnYCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae8fd7ad0af9bcf6119f7c5ef76d48bf6b13df5df311c938d0a99507a11e2046","last_reissued_at":"2026-05-17T23:44:47.947217Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:47.947217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Boosting for Comparison-Based Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Micha\\\"el Perrot, Ulrike von Luxburg","submitted_at":"2018-10-31T15:26:12Z","abstract_excerpt":"We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form \"object $x_i$ is closer to object $x_j$ than to object $x_k$.\" In this paper we introduce TripletBoost, a new method that can learn a classifier just from such triplet comparisons. The main idea is to aggregate the triplets information into weak classifiers, which can subsequently be boosted to a strong classifier. Our method has two main advantages: (i) it is applicable to data from any metric space, and (ii) it can deal with large scale probl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.13333","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":""},"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":"1810.13333","created_at":"2026-05-17T23:44:47.947303+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.13333v2","created_at":"2026-05-17T23:44:47.947303+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.13333","created_at":"2026-05-17T23:44:47.947303+00:00"},{"alias_kind":"pith_short_12","alias_value":"V2H5PLIK7G6P","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"V2H5PLIK7G6PMEM7","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"V2H5PLIK","created_at":"2026-05-18T12:32:56.356000+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/V2H5PLIK7G6PMEM7PRPPO3KIX5","json":"https://pith.science/pith/V2H5PLIK7G6PMEM7PRPPO3KIX5.json","graph_json":"https://pith.science/api/pith-number/V2H5PLIK7G6PMEM7PRPPO3KIX5/graph.json","events_json":"https://pith.science/api/pith-number/V2H5PLIK7G6PMEM7PRPPO3KIX5/events.json","paper":"https://pith.science/paper/V2H5PLIK"},"agent_actions":{"view_html":"https://pith.science/pith/V2H5PLIK7G6PMEM7PRPPO3KIX5","download_json":"https://pith.science/pith/V2H5PLIK7G6PMEM7PRPPO3KIX5.json","view_paper":"https://pith.science/paper/V2H5PLIK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.13333&json=true","fetch_graph":"https://pith.science/api/pith-number/V2H5PLIK7G6PMEM7PRPPO3KIX5/graph.json","fetch_events":"https://pith.science/api/pith-number/V2H5PLIK7G6PMEM7PRPPO3KIX5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V2H5PLIK7G6PMEM7PRPPO3KIX5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V2H5PLIK7G6PMEM7PRPPO3KIX5/action/storage_attestation","attest_author":"https://pith.science/pith/V2H5PLIK7G6PMEM7PRPPO3KIX5/action/author_attestation","sign_citation":"https://pith.science/pith/V2H5PLIK7G6PMEM7PRPPO3KIX5/action/citation_signature","submit_replication":"https://pith.science/pith/V2H5PLIK7G6PMEM7PRPPO3KIX5/action/replication_record"}},"created_at":"2026-05-17T23:44:47.947303+00:00","updated_at":"2026-05-17T23:44:47.947303+00:00"}