{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:XFPQ7WRMRXPUN3VSE4RVCWNX7I","short_pith_number":"pith:XFPQ7WRM","canonical_record":{"source":{"id":"1409.6046","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-21T21:53:08Z","cross_cats_sorted":["cs.CV","cs.IT","cs.LG","cs.NE","math.IT"],"title_canon_sha256":"82b966954fbba1d5c195240cb3cc430a3653b2e78afa1a71d817d0b2c7ee489c","abstract_canon_sha256":"bba0e4f54772a30a79a41a167e29b26a119364f08ec89c1db0a31f050a550e8f"},"schema_version":"1.0"},"canonical_sha256":"b95f0fda2c8ddf46eeb227235159b7fa0f64ad7543c60acc303d05b04c127106","source":{"kind":"arxiv","id":"1409.6046","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.6046","created_at":"2026-05-18T01:29:05Z"},{"alias_kind":"arxiv_version","alias_value":"1409.6046v1","created_at":"2026-05-18T01:29:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.6046","created_at":"2026-05-18T01:29:05Z"},{"alias_kind":"pith_short_12","alias_value":"XFPQ7WRMRXPU","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"XFPQ7WRMRXPUN3VS","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"XFPQ7WRM","created_at":"2026-05-18T12:28:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:XFPQ7WRMRXPUN3VSE4RVCWNX7I","target":"record","payload":{"canonical_record":{"source":{"id":"1409.6046","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-21T21:53:08Z","cross_cats_sorted":["cs.CV","cs.IT","cs.LG","cs.NE","math.IT"],"title_canon_sha256":"82b966954fbba1d5c195240cb3cc430a3653b2e78afa1a71d817d0b2c7ee489c","abstract_canon_sha256":"bba0e4f54772a30a79a41a167e29b26a119364f08ec89c1db0a31f050a550e8f"},"schema_version":"1.0"},"canonical_sha256":"b95f0fda2c8ddf46eeb227235159b7fa0f64ad7543c60acc303d05b04c127106","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:29:05.191198Z","signature_b64":"v7rqF0p897xZb4mNo62oDhXXv6umimJTVmdNy5Ct3Y/bJ3kz4GbT6/UWFU5zbhQS9zS9e3VdcKjRmPzRPpx2DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b95f0fda2c8ddf46eeb227235159b7fa0f64ad7543c60acc303d05b04c127106","last_reissued_at":"2026-05-18T01:29:05.190390Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:29:05.190390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1409.6046","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":"2026-05-18T01:29:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sFUj1dDT4GQNEZOaPdDLmELHuDaZiOG5fg3O5x6QAUiOaT1iD8Gs4A4VnCco4yW7J6WHzfJnG6f75szexcXaCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T09:38:01.437755Z"},"content_sha256":"3a3d6742022b47e17f68f28d5dcce57609a0b73c725bdc7dea6a1f7db01aaea4","schema_version":"1.0","event_id":"sha256:3a3d6742022b47e17f68f28d5dcce57609a0b73c725bdc7dea6a1f7db01aaea4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:XFPQ7WRMRXPUN3VSE4RVCWNX7I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Approximation errors of online sparsification criteria","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.IT","cs.LG","cs.NE","math.IT"],"primary_cat":"stat.ML","authors_text":"Paul Honeine","submitted_at":"2014-09-21T21:53:08Z","abstract_excerpt":"Many machine learning frameworks, such as resource-allocating networks, kernel-based methods, Gaussian processes, and radial-basis-function networks, require a sparsification scheme in order to address the online learning paradigm. For this purpose, several online sparsification criteria have been proposed to restrict the model definition on a subset of samples. The most known criterion is the (linear) approximation criterion, which discards any sample that can be well represented by the already contributing samples, an operation with excessive computational complexity. Several computationally"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.6046","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":"2026-05-18T01:29:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5HXTrKhq/6X3TVdWca+JhctVLxbcma8qBVVttpAi6R2YtNMCVA8l0S69g03Kf5Y2L5ezZ8SpRcbrVrHzhknICg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T09:38:01.438126Z"},"content_sha256":"2d2076747ef1a2d205e0751e227f8ac14b09a6d3a744e1f525c83626fc3e4887","schema_version":"1.0","event_id":"sha256:2d2076747ef1a2d205e0751e227f8ac14b09a6d3a744e1f525c83626fc3e4887"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XFPQ7WRMRXPUN3VSE4RVCWNX7I/bundle.json","state_url":"https://pith.science/pith/XFPQ7WRMRXPUN3VSE4RVCWNX7I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XFPQ7WRMRXPUN3VSE4RVCWNX7I/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":"2026-06-11T09:38:01Z","links":{"resolver":"https://pith.science/pith/XFPQ7WRMRXPUN3VSE4RVCWNX7I","bundle":"https://pith.science/pith/XFPQ7WRMRXPUN3VSE4RVCWNX7I/bundle.json","state":"https://pith.science/pith/XFPQ7WRMRXPUN3VSE4RVCWNX7I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XFPQ7WRMRXPUN3VSE4RVCWNX7I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:XFPQ7WRMRXPUN3VSE4RVCWNX7I","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":"bba0e4f54772a30a79a41a167e29b26a119364f08ec89c1db0a31f050a550e8f","cross_cats_sorted":["cs.CV","cs.IT","cs.LG","cs.NE","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-21T21:53:08Z","title_canon_sha256":"82b966954fbba1d5c195240cb3cc430a3653b2e78afa1a71d817d0b2c7ee489c"},"schema_version":"1.0","source":{"id":"1409.6046","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.6046","created_at":"2026-05-18T01:29:05Z"},{"alias_kind":"arxiv_version","alias_value":"1409.6046v1","created_at":"2026-05-18T01:29:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.6046","created_at":"2026-05-18T01:29:05Z"},{"alias_kind":"pith_short_12","alias_value":"XFPQ7WRMRXPU","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"XFPQ7WRMRXPUN3VS","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"XFPQ7WRM","created_at":"2026-05-18T12:28:57Z"}],"graph_snapshots":[{"event_id":"sha256:2d2076747ef1a2d205e0751e227f8ac14b09a6d3a744e1f525c83626fc3e4887","target":"graph","created_at":"2026-05-18T01:29:05Z","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":"Many machine learning frameworks, such as resource-allocating networks, kernel-based methods, Gaussian processes, and radial-basis-function networks, require a sparsification scheme in order to address the online learning paradigm. For this purpose, several online sparsification criteria have been proposed to restrict the model definition on a subset of samples. The most known criterion is the (linear) approximation criterion, which discards any sample that can be well represented by the already contributing samples, an operation with excessive computational complexity. Several computationally","authors_text":"Paul Honeine","cross_cats":["cs.CV","cs.IT","cs.LG","cs.NE","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-21T21:53:08Z","title":"Approximation errors of online sparsification criteria"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.6046","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":"sha256:3a3d6742022b47e17f68f28d5dcce57609a0b73c725bdc7dea6a1f7db01aaea4","target":"record","created_at":"2026-05-18T01:29:05Z","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":"bba0e4f54772a30a79a41a167e29b26a119364f08ec89c1db0a31f050a550e8f","cross_cats_sorted":["cs.CV","cs.IT","cs.LG","cs.NE","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-21T21:53:08Z","title_canon_sha256":"82b966954fbba1d5c195240cb3cc430a3653b2e78afa1a71d817d0b2c7ee489c"},"schema_version":"1.0","source":{"id":"1409.6046","kind":"arxiv","version":1}},"canonical_sha256":"b95f0fda2c8ddf46eeb227235159b7fa0f64ad7543c60acc303d05b04c127106","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b95f0fda2c8ddf46eeb227235159b7fa0f64ad7543c60acc303d05b04c127106","first_computed_at":"2026-05-18T01:29:05.190390Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:29:05.190390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v7rqF0p897xZb4mNo62oDhXXv6umimJTVmdNy5Ct3Y/bJ3kz4GbT6/UWFU5zbhQS9zS9e3VdcKjRmPzRPpx2DA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:29:05.191198Z","signed_message":"canonical_sha256_bytes"},"source_id":"1409.6046","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3a3d6742022b47e17f68f28d5dcce57609a0b73c725bdc7dea6a1f7db01aaea4","sha256:2d2076747ef1a2d205e0751e227f8ac14b09a6d3a744e1f525c83626fc3e4887"],"state_sha256":"ea7260925118fbd6469a1778ec319c803ffd0bf1607eb65ef7dc0d4c29ad0613"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2m+NEG4SeN8xsQ+ScYw1pK2xM2QpwrcqgsoZPCcwt4LR3h77ZV0xJMFG0+sKSx/u3gfvbkzaVjJ79pUYzNFoAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T09:38:01.440218Z","bundle_sha256":"ac6b95ecd881fef71e50851f55dbe0b294984fafdbc837adf03b8ea792ba461a"}}