{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:ZGKXU74YKNT3XBCE6XU5U3YQWP","short_pith_number":"pith:ZGKXU74Y","schema_version":"1.0","canonical_sha256":"c9957a7f985367bb8444f5e9da6f10b3cbca8576c1dbba1e87cf58f5bf18717d","source":{"kind":"arxiv","id":"1311.0222","version":2},"attestation_state":"computed","paper":{"title":"Online Learning with Multiple Operator-valued Kernels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hachem Kadri (LIF), Julien Audiffren (LIF)","submitted_at":"2013-11-01T16:51:02Z","abstract_excerpt":"We consider the problem of learning a vector-valued function f in an online learning setting. The function f is assumed to lie in a reproducing Hilbert space of operator-valued kernels. We describe two online algorithms for learning f while taking into account the output structure. A first contribution is an algorithm, ONORMA, that extends the standard kernel-based online learning algorithm NORMA from scalar-valued to operator-valued setting. We report a cumulative error bound that holds both for classification and regression. We then define a second algorithm, MONORMA, which addresses the lim"},"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":"1311.0222","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-11-01T16:51:02Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fe53ee58ffa4393a88be95e6b7002c3444a4e5ffa895e65df7a09505fca77861","abstract_canon_sha256":"adda7c7bb2784cf06067c28e96b75e8ee168b34e295a7db700b24076b4ff1bce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:07:56.242505Z","signature_b64":"wInjfp6y2j+x3pcs9axBs5O7510YdTX93UHO8nPysVy9W7clPHKM4ElanHb0ZkRCp209whHprtAU/aM7bhR8Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9957a7f985367bb8444f5e9da6f10b3cbca8576c1dbba1e87cf58f5bf18717d","last_reissued_at":"2026-05-18T03:07:56.241920Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:07:56.241920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Learning with Multiple Operator-valued Kernels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hachem Kadri (LIF), Julien Audiffren (LIF)","submitted_at":"2013-11-01T16:51:02Z","abstract_excerpt":"We consider the problem of learning a vector-valued function f in an online learning setting. The function f is assumed to lie in a reproducing Hilbert space of operator-valued kernels. We describe two online algorithms for learning f while taking into account the output structure. A first contribution is an algorithm, ONORMA, that extends the standard kernel-based online learning algorithm NORMA from scalar-valued to operator-valued setting. We report a cumulative error bound that holds both for classification and regression. We then define a second algorithm, MONORMA, which addresses the lim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.0222","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":""},"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":"1311.0222","created_at":"2026-05-18T03:07:56.242002+00:00"},{"alias_kind":"arxiv_version","alias_value":"1311.0222v2","created_at":"2026-05-18T03:07:56.242002+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.0222","created_at":"2026-05-18T03:07:56.242002+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZGKXU74YKNT3","created_at":"2026-05-18T12:28:09.283467+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZGKXU74YKNT3XBCE","created_at":"2026-05-18T12:28:09.283467+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZGKXU74Y","created_at":"2026-05-18T12:28:09.283467+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/ZGKXU74YKNT3XBCE6XU5U3YQWP","json":"https://pith.science/pith/ZGKXU74YKNT3XBCE6XU5U3YQWP.json","graph_json":"https://pith.science/api/pith-number/ZGKXU74YKNT3XBCE6XU5U3YQWP/graph.json","events_json":"https://pith.science/api/pith-number/ZGKXU74YKNT3XBCE6XU5U3YQWP/events.json","paper":"https://pith.science/paper/ZGKXU74Y"},"agent_actions":{"view_html":"https://pith.science/pith/ZGKXU74YKNT3XBCE6XU5U3YQWP","download_json":"https://pith.science/pith/ZGKXU74YKNT3XBCE6XU5U3YQWP.json","view_paper":"https://pith.science/paper/ZGKXU74Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1311.0222&json=true","fetch_graph":"https://pith.science/api/pith-number/ZGKXU74YKNT3XBCE6XU5U3YQWP/graph.json","fetch_events":"https://pith.science/api/pith-number/ZGKXU74YKNT3XBCE6XU5U3YQWP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZGKXU74YKNT3XBCE6XU5U3YQWP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZGKXU74YKNT3XBCE6XU5U3YQWP/action/storage_attestation","attest_author":"https://pith.science/pith/ZGKXU74YKNT3XBCE6XU5U3YQWP/action/author_attestation","sign_citation":"https://pith.science/pith/ZGKXU74YKNT3XBCE6XU5U3YQWP/action/citation_signature","submit_replication":"https://pith.science/pith/ZGKXU74YKNT3XBCE6XU5U3YQWP/action/replication_record"}},"created_at":"2026-05-18T03:07:56.242002+00:00","updated_at":"2026-05-18T03:07:56.242002+00:00"}