{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3Z65E2AJFSXO3H6YC2RTLQJEGE","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":"43bdcc32db415bd75ddd363f3585359e102361f3ce047f08f18bd1eda93219b0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-16T10:48:00Z","title_canon_sha256":"7564bdd3d28e69afd9f4821264d519268dd90d7213916f513e63b89d7b8bd5d1"},"schema_version":"1.0","source":{"id":"1909.07115","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.07115","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"arxiv_version","alias_value":"1909.07115v1","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.07115","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"pith_short_12","alias_value":"3Z65E2AJFSXO","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"pith_short_16","alias_value":"3Z65E2AJFSXO3H6Y","created_at":"2026-07-05T00:04:53Z"},{"alias_kind":"pith_short_8","alias_value":"3Z65E2AJ","created_at":"2026-07-05T00:04:53Z"}],"graph_snapshots":[{"event_id":"sha256:ee543f66e0771cb048fd895dc89870c947832acf893450eed07a502ab80bac3e","target":"graph","created_at":"2026-07-05T00:04:53Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1909.07115/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose an AdaBoost-assisted extreme learning machine for efficient online sequential classification (AOS-ELM). In order to achieve better accuracy in online sequential learning scenarios, we utilize the cost-sensitive algorithm-AdaBoost, which diversifying the weak classifiers, and adding the forgetting mechanism, which stabilizing the performance during the training procedure. Hence, AOS-ELM adapts better to sequentially arrived data compared with other voting based methods. The experiment results show AOS-ELM can achieve 94.41% accuracy on MNIST dataset, which is the theor","authors_text":"An-Yeu (Andy) Wu, Yi-Ta Chen, Yu-Chuan Chuang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-16T10:48:00Z","title":"AdaBoost-assisted Extreme Learning Machine for Efficient Online Sequential Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.07115","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":"sha256:616cfe09c6abf7833f46b1c487d2d6cd906c521d1b9152d58c6fe4866aafb02b","target":"record","created_at":"2026-07-05T00:04:53Z","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":"43bdcc32db415bd75ddd363f3585359e102361f3ce047f08f18bd1eda93219b0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-16T10:48:00Z","title_canon_sha256":"7564bdd3d28e69afd9f4821264d519268dd90d7213916f513e63b89d7b8bd5d1"},"schema_version":"1.0","source":{"id":"1909.07115","kind":"arxiv","version":1}},"canonical_sha256":"de7dd268092caeed9fd816a335c1243123266160b893fd15b4c08d4629c41eb4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de7dd268092caeed9fd816a335c1243123266160b893fd15b4c08d4629c41eb4","first_computed_at":"2026-07-05T00:04:53.185766Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:04:53.185766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tUFqH6kYuY0YYC5ELqvaMhaYcVJhiPBL9TT3QxSt7IzRLxT5a7YTJpq/XNGm8efFRdsw9VB6gMOaRM/GN17WDg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:04:53.186107Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.07115","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:616cfe09c6abf7833f46b1c487d2d6cd906c521d1b9152d58c6fe4866aafb02b","sha256:ee543f66e0771cb048fd895dc89870c947832acf893450eed07a502ab80bac3e"],"state_sha256":"fc2868c87777c693a9c0bb00bc89ef80e78842c2eeecf3f22b637c46bb183426"}