{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:SHCP7MLYUNAHVVV4LNFLZEXPSA","short_pith_number":"pith:SHCP7MLY","schema_version":"1.0","canonical_sha256":"91c4ffb178a3407ad6bc5b4abc92ef900713afb353fca7d2009303fa66fb2fd7","source":{"kind":"arxiv","id":"1501.06284","version":1},"attestation_state":"computed","paper":{"title":"On a Family of Decomposable Kernels on Sequences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andrea Baisero, Carl Henrik Ek, Florian T. Pokorny","submitted_at":"2015-01-26T08:30:55Z","abstract_excerpt":"In many applications data is naturally presented in terms of orderings of some basic elements or symbols. Reasoning about such data requires a notion of similarity capable of handling sequences of different lengths. In this paper we describe a family of Mercer kernel functions for such sequentially structured data. The family is characterized by a decomposable structure in terms of symbol-level and structure-level similarities, representing a specific combination of kernels which allows for efficient computation. We provide an experimental evaluation on sequential classification tasks comparin"},"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":"1501.06284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-01-26T08:30:55Z","cross_cats_sorted":[],"title_canon_sha256":"f5c6d9340466fa780aa030f26acd4e6b35b86581ae9b05b2711f0ef4960a98cf","abstract_canon_sha256":"08067da4b0e1b3fc3dc5a1f5678cdc663f6153059ac2b079a9a17ee00bb838d6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:28:41.831073Z","signature_b64":"YwwAnOJgQXHQ+LHXIsMqmNFsQYvzBAwJcY/Acnf1zadRJZ9viG4PdiFRVIUFudAyoxIKiS7DA5DTOwFi5SXCBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91c4ffb178a3407ad6bc5b4abc92ef900713afb353fca7d2009303fa66fb2fd7","last_reissued_at":"2026-05-18T02:28:41.830644Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:28:41.830644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On a Family of Decomposable Kernels on Sequences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andrea Baisero, Carl Henrik Ek, Florian T. Pokorny","submitted_at":"2015-01-26T08:30:55Z","abstract_excerpt":"In many applications data is naturally presented in terms of orderings of some basic elements or symbols. Reasoning about such data requires a notion of similarity capable of handling sequences of different lengths. In this paper we describe a family of Mercer kernel functions for such sequentially structured data. The family is characterized by a decomposable structure in terms of symbol-level and structure-level similarities, representing a specific combination of kernels which allows for efficient computation. We provide an experimental evaluation on sequential classification tasks comparin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.06284","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1501.06284","created_at":"2026-05-18T02:28:41.830710+00:00"},{"alias_kind":"arxiv_version","alias_value":"1501.06284v1","created_at":"2026-05-18T02:28:41.830710+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.06284","created_at":"2026-05-18T02:28:41.830710+00:00"},{"alias_kind":"pith_short_12","alias_value":"SHCP7MLYUNAH","created_at":"2026-05-18T12:29:42.218222+00:00"},{"alias_kind":"pith_short_16","alias_value":"SHCP7MLYUNAHVVV4","created_at":"2026-05-18T12:29:42.218222+00:00"},{"alias_kind":"pith_short_8","alias_value":"SHCP7MLY","created_at":"2026-05-18T12:29:42.218222+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.09716","citing_title":"Medical Model Synthesis Architectures: A Case Study","ref_index":298,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SHCP7MLYUNAHVVV4LNFLZEXPSA","json":"https://pith.science/pith/SHCP7MLYUNAHVVV4LNFLZEXPSA.json","graph_json":"https://pith.science/api/pith-number/SHCP7MLYUNAHVVV4LNFLZEXPSA/graph.json","events_json":"https://pith.science/api/pith-number/SHCP7MLYUNAHVVV4LNFLZEXPSA/events.json","paper":"https://pith.science/paper/SHCP7MLY"},"agent_actions":{"view_html":"https://pith.science/pith/SHCP7MLYUNAHVVV4LNFLZEXPSA","download_json":"https://pith.science/pith/SHCP7MLYUNAHVVV4LNFLZEXPSA.json","view_paper":"https://pith.science/paper/SHCP7MLY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1501.06284&json=true","fetch_graph":"https://pith.science/api/pith-number/SHCP7MLYUNAHVVV4LNFLZEXPSA/graph.json","fetch_events":"https://pith.science/api/pith-number/SHCP7MLYUNAHVVV4LNFLZEXPSA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SHCP7MLYUNAHVVV4LNFLZEXPSA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SHCP7MLYUNAHVVV4LNFLZEXPSA/action/storage_attestation","attest_author":"https://pith.science/pith/SHCP7MLYUNAHVVV4LNFLZEXPSA/action/author_attestation","sign_citation":"https://pith.science/pith/SHCP7MLYUNAHVVV4LNFLZEXPSA/action/citation_signature","submit_replication":"https://pith.science/pith/SHCP7MLYUNAHVVV4LNFLZEXPSA/action/replication_record"}},"created_at":"2026-05-18T02:28:41.830710+00:00","updated_at":"2026-05-18T02:28:41.830710+00:00"}