{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:XI32QD6GWOY3USLEBBIZYSXI7X","short_pith_number":"pith:XI32QD6G","canonical_record":{"source":{"id":"1604.06518","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-22T01:57:01Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3862ddeafefac87bec8206ece6e8ea159c90f9ef1c2dd5f054a4adadd8cb0775","abstract_canon_sha256":"0ba51023e9e58eed4b4b4212da4166084dbe4b478c721b393dd0eb88fd21570d"},"schema_version":"1.0"},"canonical_sha256":"ba37a80fc6b3b1ba496408519c4ae8fdd5c22d88239c6f9a4ed25aed098fb68b","source":{"kind":"arxiv","id":"1604.06518","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.06518","created_at":"2026-05-18T00:43:35Z"},{"alias_kind":"arxiv_version","alias_value":"1604.06518v4","created_at":"2026-05-18T00:43:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.06518","created_at":"2026-05-18T00:43:35Z"},{"alias_kind":"pith_short_12","alias_value":"XI32QD6GWOY3","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"XI32QD6GWOY3USLE","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"XI32QD6G","created_at":"2026-05-18T12:30:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:XI32QD6GWOY3USLEBBIZYSXI7X","target":"record","payload":{"canonical_record":{"source":{"id":"1604.06518","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-22T01:57:01Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3862ddeafefac87bec8206ece6e8ea159c90f9ef1c2dd5f054a4adadd8cb0775","abstract_canon_sha256":"0ba51023e9e58eed4b4b4212da4166084dbe4b478c721b393dd0eb88fd21570d"},"schema_version":"1.0"},"canonical_sha256":"ba37a80fc6b3b1ba496408519c4ae8fdd5c22d88239c6f9a4ed25aed098fb68b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:35.844755Z","signature_b64":"PL2RVeM3CDe3hDdkY84bRmyEMAUgI9G4Ltp3EXQQOkmJYcSVuGoA+Dz4RmeLXhpoDHOKrwRvLerhlA4lXWD3AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba37a80fc6b3b1ba496408519c4ae8fdd5c22d88239c6f9a4ed25aed098fb68b","last_reissued_at":"2026-05-18T00:43:35.844389Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:35.844389Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1604.06518","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:43:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hxCpPtQZXT59+xPukeoWY3sr/TV2npIKNr8eOXO+PVucEyClFDZeFk9oxMnuE9cJwQTM7chdhZRFRap09pAUDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T01:02:50.279837Z"},"content_sha256":"4acf227110f45853c47d8f163ecd64a13f74649e7ae1e792925c2198dfa740d3","schema_version":"1.0","event_id":"sha256:4acf227110f45853c47d8f163ecd64a13f74649e7ae1e792925c2198dfa740d3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:XI32QD6GWOY3USLEBBIZYSXI7X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Approximation Vector Machines for Large-scale Online Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dinh Phung, Trung Le, Tu Dinh Nguyen, Vu Nguyen","submitted_at":"2016-04-22T01:57:01Z","abstract_excerpt":"One of the most challenging problems in kernel online learning is to bound the model size and to promote the model sparsity. Sparse models not only improve computation and memory usage, but also enhance the generalization capacity, a principle that concurs with the law of parsimony. However, inappropriate sparsity modeling may also significantly degrade the performance. In this paper, we propose Approximation Vector Machine (AVM), a model that can simultaneously encourage the sparsity and safeguard its risk in compromising the performance. When an incoming instance arrives, we approximate this"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.06518","kind":"arxiv","version":4},"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":"2026-05-18T00:43:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Emx3NIinrqrN2+at453BXldJ6zjWkhENJhYR2F+CZO+u1CxXGV3Dl1bNPuKVDsfpmGz0ZO0Ocet1bs9HXQ6wDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T01:02:50.280501Z"},"content_sha256":"695a270b62a481c02460e8e6b6f8c9bc9f9e3576b7a6a988dfa9fd81146827f9","schema_version":"1.0","event_id":"sha256:695a270b62a481c02460e8e6b6f8c9bc9f9e3576b7a6a988dfa9fd81146827f9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XI32QD6GWOY3USLEBBIZYSXI7X/bundle.json","state_url":"https://pith.science/pith/XI32QD6GWOY3USLEBBIZYSXI7X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XI32QD6GWOY3USLEBBIZYSXI7X/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":"2026-06-05T01:02:50Z","links":{"resolver":"https://pith.science/pith/XI32QD6GWOY3USLEBBIZYSXI7X","bundle":"https://pith.science/pith/XI32QD6GWOY3USLEBBIZYSXI7X/bundle.json","state":"https://pith.science/pith/XI32QD6GWOY3USLEBBIZYSXI7X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XI32QD6GWOY3USLEBBIZYSXI7X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:XI32QD6GWOY3USLEBBIZYSXI7X","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":"0ba51023e9e58eed4b4b4212da4166084dbe4b478c721b393dd0eb88fd21570d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-22T01:57:01Z","title_canon_sha256":"3862ddeafefac87bec8206ece6e8ea159c90f9ef1c2dd5f054a4adadd8cb0775"},"schema_version":"1.0","source":{"id":"1604.06518","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.06518","created_at":"2026-05-18T00:43:35Z"},{"alias_kind":"arxiv_version","alias_value":"1604.06518v4","created_at":"2026-05-18T00:43:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.06518","created_at":"2026-05-18T00:43:35Z"},{"alias_kind":"pith_short_12","alias_value":"XI32QD6GWOY3","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"XI32QD6GWOY3USLE","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"XI32QD6G","created_at":"2026-05-18T12:30:51Z"}],"graph_snapshots":[{"event_id":"sha256:695a270b62a481c02460e8e6b6f8c9bc9f9e3576b7a6a988dfa9fd81146827f9","target":"graph","created_at":"2026-05-18T00:43:35Z","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":"One of the most challenging problems in kernel online learning is to bound the model size and to promote the model sparsity. Sparse models not only improve computation and memory usage, but also enhance the generalization capacity, a principle that concurs with the law of parsimony. However, inappropriate sparsity modeling may also significantly degrade the performance. In this paper, we propose Approximation Vector Machine (AVM), a model that can simultaneously encourage the sparsity and safeguard its risk in compromising the performance. When an incoming instance arrives, we approximate this","authors_text":"Dinh Phung, Trung Le, Tu Dinh Nguyen, Vu Nguyen","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-22T01:57:01Z","title":"Approximation Vector Machines for Large-scale Online Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.06518","kind":"arxiv","version":4},"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":"sha256:4acf227110f45853c47d8f163ecd64a13f74649e7ae1e792925c2198dfa740d3","target":"record","created_at":"2026-05-18T00:43:35Z","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":"0ba51023e9e58eed4b4b4212da4166084dbe4b478c721b393dd0eb88fd21570d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-22T01:57:01Z","title_canon_sha256":"3862ddeafefac87bec8206ece6e8ea159c90f9ef1c2dd5f054a4adadd8cb0775"},"schema_version":"1.0","source":{"id":"1604.06518","kind":"arxiv","version":4}},"canonical_sha256":"ba37a80fc6b3b1ba496408519c4ae8fdd5c22d88239c6f9a4ed25aed098fb68b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ba37a80fc6b3b1ba496408519c4ae8fdd5c22d88239c6f9a4ed25aed098fb68b","first_computed_at":"2026-05-18T00:43:35.844389Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:35.844389Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PL2RVeM3CDe3hDdkY84bRmyEMAUgI9G4Ltp3EXQQOkmJYcSVuGoA+Dz4RmeLXhpoDHOKrwRvLerhlA4lXWD3AA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:35.844755Z","signed_message":"canonical_sha256_bytes"},"source_id":"1604.06518","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4acf227110f45853c47d8f163ecd64a13f74649e7ae1e792925c2198dfa740d3","sha256:695a270b62a481c02460e8e6b6f8c9bc9f9e3576b7a6a988dfa9fd81146827f9"],"state_sha256":"e540448998c0c786a62372fb45294cb9aa6ae8cea451e7204e01debfebf6fd98"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LaC+I/aGB5hfs71Y15XoR9MF2/YJR9eFWu2kbH1WYDhGTjAGvV3MVvHDPiLBf6OiPDXHjSuMU6QS9p+J/ywfCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T01:02:50.285083Z","bundle_sha256":"5117a3959a43be2a032f0d573ef16b1e66d9c6beda17fba3f6fc1beb16a3eabe"}}