{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:LUYDALB372TNQ56AMYZYTMIR3W","short_pith_number":"pith:LUYDALB3","canonical_record":{"source":{"id":"1402.1349","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T13:35:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2dea32068c16ce278ed02f2a52c3345cbf7ce4fc25973dc03d3888e9722e13e4","abstract_canon_sha256":"98e14606b9d493f08fc97e7624109dd1eb2a2613997ff1faf8ccbd6589703f97"},"schema_version":"1.0"},"canonical_sha256":"5d30302c3bfea6d877c0663389b111ddbd62762e2d2f87970919ea79a791af46","source":{"kind":"arxiv","id":"1402.1349","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.1349","created_at":"2026-05-18T01:11:18Z"},{"alias_kind":"arxiv_version","alias_value":"1402.1349v1","created_at":"2026-05-18T01:11:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.1349","created_at":"2026-05-18T01:11:18Z"},{"alias_kind":"pith_short_12","alias_value":"LUYDALB372TN","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_16","alias_value":"LUYDALB372TNQ56A","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_8","alias_value":"LUYDALB3","created_at":"2026-05-18T12:28:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:LUYDALB372TNQ56AMYZYTMIR3W","target":"record","payload":{"canonical_record":{"source":{"id":"1402.1349","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T13:35:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2dea32068c16ce278ed02f2a52c3345cbf7ce4fc25973dc03d3888e9722e13e4","abstract_canon_sha256":"98e14606b9d493f08fc97e7624109dd1eb2a2613997ff1faf8ccbd6589703f97"},"schema_version":"1.0"},"canonical_sha256":"5d30302c3bfea6d877c0663389b111ddbd62762e2d2f87970919ea79a791af46","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:11:18.673192Z","signature_b64":"VCxaj9OobamnS1kzzzc+kkrijmgmLl3oV6vq4QsKCH9Xqe8gNYLOSEnLqqUajfI92TAUINCGYmYMEXKPasHOBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d30302c3bfea6d877c0663389b111ddbd62762e2d2f87970919ea79a791af46","last_reissued_at":"2026-05-18T01:11:18.672786Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:11:18.672786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1402.1349","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:11:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eOe8AK69yVduylYY59ztKl7jlGybWFS0Ot3QRZaHNGmIX3NpfxEeHa+qH5rEuYGIPMzJjRTPIahinzRuavDyAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T14:01:44.960122Z"},"content_sha256":"943b32a4984f3d246dd53271257f55ba2a47d7bbfc7823910c7b20a11c06940a","schema_version":"1.0","event_id":"sha256:943b32a4984f3d246dd53271257f55ba2a47d7bbfc7823910c7b20a11c06940a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:LUYDALB372TNQ56AMYZYTMIR3W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dissimilarity-based Ensembles for Multiple Instance Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"David M. J. Tax, Marco Loog, Veronika Cheplygina","submitted_at":"2014-02-06T13:35:01Z","abstract_excerpt":"In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation determined by the number of training bags, while the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. In"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.1349","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:11:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l8k+uf02xOLyxShDMN+dWJIw2NaaWMn8GeLDePzb7HnTm/bswwBGoSVYzTzY3DicS09WYCh4WInuhRnIQi0PAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T14:01:44.960827Z"},"content_sha256":"f50413629c54838b07f262f1505209dc5d29cc5b70b02966716861d6295292e5","schema_version":"1.0","event_id":"sha256:f50413629c54838b07f262f1505209dc5d29cc5b70b02966716861d6295292e5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LUYDALB372TNQ56AMYZYTMIR3W/bundle.json","state_url":"https://pith.science/pith/LUYDALB372TNQ56AMYZYTMIR3W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LUYDALB372TNQ56AMYZYTMIR3W/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T14:01:44Z","links":{"resolver":"https://pith.science/pith/LUYDALB372TNQ56AMYZYTMIR3W","bundle":"https://pith.science/pith/LUYDALB372TNQ56AMYZYTMIR3W/bundle.json","state":"https://pith.science/pith/LUYDALB372TNQ56AMYZYTMIR3W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LUYDALB372TNQ56AMYZYTMIR3W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:LUYDALB372TNQ56AMYZYTMIR3W","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"98e14606b9d493f08fc97e7624109dd1eb2a2613997ff1faf8ccbd6589703f97","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T13:35:01Z","title_canon_sha256":"2dea32068c16ce278ed02f2a52c3345cbf7ce4fc25973dc03d3888e9722e13e4"},"schema_version":"1.0","source":{"id":"1402.1349","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.1349","created_at":"2026-05-18T01:11:18Z"},{"alias_kind":"arxiv_version","alias_value":"1402.1349v1","created_at":"2026-05-18T01:11:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.1349","created_at":"2026-05-18T01:11:18Z"},{"alias_kind":"pith_short_12","alias_value":"LUYDALB372TN","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_16","alias_value":"LUYDALB372TNQ56A","created_at":"2026-05-18T12:28:38Z"},{"alias_kind":"pith_short_8","alias_value":"LUYDALB3","created_at":"2026-05-18T12:28:38Z"}],"graph_snapshots":[{"event_id":"sha256:f50413629c54838b07f262f1505209dc5d29cc5b70b02966716861d6295292e5","target":"graph","created_at":"2026-05-18T01:11:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation determined by the number of training bags, while the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. In","authors_text":"David M. J. Tax, Marco Loog, Veronika Cheplygina","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T13:35:01Z","title":"Dissimilarity-based Ensembles for Multiple Instance Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.1349","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:943b32a4984f3d246dd53271257f55ba2a47d7bbfc7823910c7b20a11c06940a","target":"record","created_at":"2026-05-18T01:11:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"98e14606b9d493f08fc97e7624109dd1eb2a2613997ff1faf8ccbd6589703f97","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-06T13:35:01Z","title_canon_sha256":"2dea32068c16ce278ed02f2a52c3345cbf7ce4fc25973dc03d3888e9722e13e4"},"schema_version":"1.0","source":{"id":"1402.1349","kind":"arxiv","version":1}},"canonical_sha256":"5d30302c3bfea6d877c0663389b111ddbd62762e2d2f87970919ea79a791af46","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5d30302c3bfea6d877c0663389b111ddbd62762e2d2f87970919ea79a791af46","first_computed_at":"2026-05-18T01:11:18.672786Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:11:18.672786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VCxaj9OobamnS1kzzzc+kkrijmgmLl3oV6vq4QsKCH9Xqe8gNYLOSEnLqqUajfI92TAUINCGYmYMEXKPasHOBA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:11:18.673192Z","signed_message":"canonical_sha256_bytes"},"source_id":"1402.1349","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:943b32a4984f3d246dd53271257f55ba2a47d7bbfc7823910c7b20a11c06940a","sha256:f50413629c54838b07f262f1505209dc5d29cc5b70b02966716861d6295292e5"],"state_sha256":"251aa09adac12bd66f053723ed26d4a2e75cb713cf34fb58e3eea30579ce09d6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mv8TmQwMXVgHsrW2/4QjUlLXwlYQnGBf2zBwz26p/ZP6f0l7+ZYc9l0AIDja6dEpx7u0plWsyWKpYaPScTSpAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T14:01:44.965018Z","bundle_sha256":"2bacba311d9f23541b017e9fe2bdcb10d11dd3390a2f65656c305d97dc57e880"}}