{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:D2EWWJMSWAH32VDYE64M3WZOQW","short_pith_number":"pith:D2EWWJMS","schema_version":"1.0","canonical_sha256":"1e896b2592b00fbd547827b8cddb2e85be5c33d6a2e8b1ebf025d449990a059e","source":{"kind":"arxiv","id":"2003.11333","version":2},"attestation_state":"computed","paper":{"title":"Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bogdan Gabrys, Thanh Tung Khuat","submitted_at":"2020-03-25T11:26:18Z","abstract_excerpt":"This paper proposes a method to accelerate the training process of a general fuzzy min-max neural network. The purpose is to reduce the unsuitable hyperboxes selected as the potential candidates of the expansion step of existing hyperboxes to cover a new input pattern in the online learning algorithms or candidates of the hyperbox aggregation process in the agglomerative learning algorithms. Our proposed approach is based on the mathematical formulas to form a branch-and-bound solution aiming to remove the hyperboxes which are certain not to satisfy expansion or aggregation conditions, and in "},"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":"2003.11333","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2020-03-25T11:26:18Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3818af1e6c86803cbc5021adfc015f93d095a50082163e86100c2aec6c42eaae","abstract_canon_sha256":"1d06175a6e240c5d4d1387a9b9119507ab15bbd1aaec39a21f6af1bdb27d6ac3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:03:43.710696Z","signature_b64":"LDm4kIr4JZdaE+wuKHtRMNuWHO4pbjThJmnhTcaSlIAq4ZA45r/Iz9mPK1cp1VXI74gsKA/zHZqaO6TZ2xUXBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e896b2592b00fbd547827b8cddb2e85be5c33d6a2e8b1ebf025d449990a059e","last_reissued_at":"2026-07-05T01:03:43.709914Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:03:43.709914Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bogdan Gabrys, Thanh Tung Khuat","submitted_at":"2020-03-25T11:26:18Z","abstract_excerpt":"This paper proposes a method to accelerate the training process of a general fuzzy min-max neural network. The purpose is to reduce the unsuitable hyperboxes selected as the potential candidates of the expansion step of existing hyperboxes to cover a new input pattern in the online learning algorithms or candidates of the hyperbox aggregation process in the agglomerative learning algorithms. Our proposed approach is based on the mathematical formulas to form a branch-and-bound solution aiming to remove the hyperboxes which are certain not to satisfy expansion or aggregation conditions, and in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.11333","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/2003.11333/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":"2003.11333","created_at":"2026-07-05T01:03:43.709993+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.11333v2","created_at":"2026-07-05T01:03:43.709993+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.11333","created_at":"2026-07-05T01:03:43.709993+00:00"},{"alias_kind":"pith_short_12","alias_value":"D2EWWJMSWAH3","created_at":"2026-07-05T01:03:43.709993+00:00"},{"alias_kind":"pith_short_16","alias_value":"D2EWWJMSWAH32VDY","created_at":"2026-07-05T01:03:43.709993+00:00"},{"alias_kind":"pith_short_8","alias_value":"D2EWWJMS","created_at":"2026-07-05T01:03:43.709993+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/D2EWWJMSWAH32VDYE64M3WZOQW","json":"https://pith.science/pith/D2EWWJMSWAH32VDYE64M3WZOQW.json","graph_json":"https://pith.science/api/pith-number/D2EWWJMSWAH32VDYE64M3WZOQW/graph.json","events_json":"https://pith.science/api/pith-number/D2EWWJMSWAH32VDYE64M3WZOQW/events.json","paper":"https://pith.science/paper/D2EWWJMS"},"agent_actions":{"view_html":"https://pith.science/pith/D2EWWJMSWAH32VDYE64M3WZOQW","download_json":"https://pith.science/pith/D2EWWJMSWAH32VDYE64M3WZOQW.json","view_paper":"https://pith.science/paper/D2EWWJMS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.11333&json=true","fetch_graph":"https://pith.science/api/pith-number/D2EWWJMSWAH32VDYE64M3WZOQW/graph.json","fetch_events":"https://pith.science/api/pith-number/D2EWWJMSWAH32VDYE64M3WZOQW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D2EWWJMSWAH32VDYE64M3WZOQW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D2EWWJMSWAH32VDYE64M3WZOQW/action/storage_attestation","attest_author":"https://pith.science/pith/D2EWWJMSWAH32VDYE64M3WZOQW/action/author_attestation","sign_citation":"https://pith.science/pith/D2EWWJMSWAH32VDYE64M3WZOQW/action/citation_signature","submit_replication":"https://pith.science/pith/D2EWWJMSWAH32VDYE64M3WZOQW/action/replication_record"}},"created_at":"2026-07-05T01:03:43.709993+00:00","updated_at":"2026-07-05T01:03:43.709993+00:00"}