{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2006:FMWRCDBCFHUWQFT7EUBXXZ6JS6","short_pith_number":"pith:FMWRCDBC","canonical_record":{"source":{"id":"cs/0611135","kind":"arxiv","version":1},"metadata":{"license":"","primary_cat":"cs.AI","submitted_at":"2006-11-27T14:38:44Z","cross_cats_sorted":[],"title_canon_sha256":"0362e3a0797850472481af344513408d9472d877476f33318560fcebca65b35a","abstract_canon_sha256":"0872469a5cad8da5e3818f6ae6b478a46e80515343c667d1f4b478b903d396dd"},"schema_version":"1.0"},"canonical_sha256":"2b2d110c2229e968167f25037be7c99797e4fbaac33aacb3b11398bff52a7c21","source":{"kind":"arxiv","id":"cs/0611135","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"cs/0611135","created_at":"2026-05-18T01:08:54Z"},{"alias_kind":"arxiv_version","alias_value":"cs/0611135v1","created_at":"2026-05-18T01:08:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.cs/0611135","created_at":"2026-05-18T01:08:54Z"},{"alias_kind":"pith_short_12","alias_value":"FMWRCDBCFHUW","created_at":"2026-05-18T12:25:53Z"},{"alias_kind":"pith_short_16","alias_value":"FMWRCDBCFHUWQFT7","created_at":"2026-05-18T12:25:53Z"},{"alias_kind":"pith_short_8","alias_value":"FMWRCDBC","created_at":"2026-05-18T12:25:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2006:FMWRCDBCFHUWQFT7EUBXXZ6JS6","target":"record","payload":{"canonical_record":{"source":{"id":"cs/0611135","kind":"arxiv","version":1},"metadata":{"license":"","primary_cat":"cs.AI","submitted_at":"2006-11-27T14:38:44Z","cross_cats_sorted":[],"title_canon_sha256":"0362e3a0797850472481af344513408d9472d877476f33318560fcebca65b35a","abstract_canon_sha256":"0872469a5cad8da5e3818f6ae6b478a46e80515343c667d1f4b478b903d396dd"},"schema_version":"1.0"},"canonical_sha256":"2b2d110c2229e968167f25037be7c99797e4fbaac33aacb3b11398bff52a7c21","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:08:54.209762Z","signature_b64":"34yIyRosunx8cha0n607aSJB3ZEGGMT+LoE0kyK5YsVh3xXnGjnguSo/PEHIl0UHHvgLJlZUNelC58OWIKnIAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b2d110c2229e968167f25037be7c99797e4fbaac33aacb3b11398bff52a7c21","last_reissued_at":"2026-05-18T01:08:54.209076Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:08:54.209076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"cs/0611135","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":"2026-05-18T01:08:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"okB54cQDVg7edAm0ooGBZ2XDIGUYggqg59VeR10QlkHu/PVc+eaTed57xtd8IdukOKreoTKf242gPkEEXXuSCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T21:16:59.228767Z"},"content_sha256":"6fa8c54ca3a81c9a10c3f0b50e6155c306323440a56e2f370467ce42af85be7c","schema_version":"1.0","event_id":"sha256:6fa8c54ca3a81c9a10c3f0b50e6155c306323440a56e2f370467ce42af85be7c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2006:FMWRCDBCFHUWQFT7EUBXXZ6JS6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection","license":"","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Christian Gagn\\'e (INRIA Futurs, ISI), LRI), Marco Tomassini (ISI), Marc Schoenauer (INRIA Futurs, Mich\\`ele Sebag (LRI)","submitted_at":"2006-11-27T14:38:44Z","abstract_excerpt":"Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed optimization problem; ii) non-linear learning can be brought into linear learning thanks to the kernel trick and the mapping of the initial search space onto a high dimensional feature space. The kernel is designed by the ML expert and it governs the efficiency of the SVM approach. In this paper, a new approach for the automatic design of kernels by Genetic Programming, called the Evolutionary Kernel Machine (EKM), is presented. E"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"cs/0611135","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":"2026-05-18T01:08:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S1ZnmZ3esfPt7dPnp1GZTU6ujSjDFQkpcei+hPWjZxs0vuRQwZ0ozRHNEKBo4zh12mv7lziDanVC50wqr76aBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T21:16:59.229113Z"},"content_sha256":"d52953bd852048250dfe3f8f83e366efd8e905c17ef560dc92eddb7f31cfc7ae","schema_version":"1.0","event_id":"sha256:d52953bd852048250dfe3f8f83e366efd8e905c17ef560dc92eddb7f31cfc7ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FMWRCDBCFHUWQFT7EUBXXZ6JS6/bundle.json","state_url":"https://pith.science/pith/FMWRCDBCFHUWQFT7EUBXXZ6JS6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FMWRCDBCFHUWQFT7EUBXXZ6JS6/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":"2026-06-03T21:16:59Z","links":{"resolver":"https://pith.science/pith/FMWRCDBCFHUWQFT7EUBXXZ6JS6","bundle":"https://pith.science/pith/FMWRCDBCFHUWQFT7EUBXXZ6JS6/bundle.json","state":"https://pith.science/pith/FMWRCDBCFHUWQFT7EUBXXZ6JS6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FMWRCDBCFHUWQFT7EUBXXZ6JS6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2006:FMWRCDBCFHUWQFT7EUBXXZ6JS6","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":"0872469a5cad8da5e3818f6ae6b478a46e80515343c667d1f4b478b903d396dd","cross_cats_sorted":[],"license":"","primary_cat":"cs.AI","submitted_at":"2006-11-27T14:38:44Z","title_canon_sha256":"0362e3a0797850472481af344513408d9472d877476f33318560fcebca65b35a"},"schema_version":"1.0","source":{"id":"cs/0611135","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"cs/0611135","created_at":"2026-05-18T01:08:54Z"},{"alias_kind":"arxiv_version","alias_value":"cs/0611135v1","created_at":"2026-05-18T01:08:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.cs/0611135","created_at":"2026-05-18T01:08:54Z"},{"alias_kind":"pith_short_12","alias_value":"FMWRCDBCFHUW","created_at":"2026-05-18T12:25:53Z"},{"alias_kind":"pith_short_16","alias_value":"FMWRCDBCFHUWQFT7","created_at":"2026-05-18T12:25:53Z"},{"alias_kind":"pith_short_8","alias_value":"FMWRCDBC","created_at":"2026-05-18T12:25:53Z"}],"graph_snapshots":[{"event_id":"sha256:d52953bd852048250dfe3f8f83e366efd8e905c17ef560dc92eddb7f31cfc7ae","target":"graph","created_at":"2026-05-18T01:08:54Z","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":"Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed optimization problem; ii) non-linear learning can be brought into linear learning thanks to the kernel trick and the mapping of the initial search space onto a high dimensional feature space. The kernel is designed by the ML expert and it governs the efficiency of the SVM approach. In this paper, a new approach for the automatic design of kernels by Genetic Programming, called the Evolutionary Kernel Machine (EKM), is presented. E","authors_text":"Christian Gagn\\'e (INRIA Futurs, ISI), LRI), Marco Tomassini (ISI), Marc Schoenauer (INRIA Futurs, Mich\\`ele Sebag (LRI)","cross_cats":[],"headline":"","license":"","primary_cat":"cs.AI","submitted_at":"2006-11-27T14:38:44Z","title":"Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"cs/0611135","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":"sha256:6fa8c54ca3a81c9a10c3f0b50e6155c306323440a56e2f370467ce42af85be7c","target":"record","created_at":"2026-05-18T01:08:54Z","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":"0872469a5cad8da5e3818f6ae6b478a46e80515343c667d1f4b478b903d396dd","cross_cats_sorted":[],"license":"","primary_cat":"cs.AI","submitted_at":"2006-11-27T14:38:44Z","title_canon_sha256":"0362e3a0797850472481af344513408d9472d877476f33318560fcebca65b35a"},"schema_version":"1.0","source":{"id":"cs/0611135","kind":"arxiv","version":1}},"canonical_sha256":"2b2d110c2229e968167f25037be7c99797e4fbaac33aacb3b11398bff52a7c21","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2b2d110c2229e968167f25037be7c99797e4fbaac33aacb3b11398bff52a7c21","first_computed_at":"2026-05-18T01:08:54.209076Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:08:54.209076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"34yIyRosunx8cha0n607aSJB3ZEGGMT+LoE0kyK5YsVh3xXnGjnguSo/PEHIl0UHHvgLJlZUNelC58OWIKnIAg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:08:54.209762Z","signed_message":"canonical_sha256_bytes"},"source_id":"cs/0611135","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6fa8c54ca3a81c9a10c3f0b50e6155c306323440a56e2f370467ce42af85be7c","sha256:d52953bd852048250dfe3f8f83e366efd8e905c17ef560dc92eddb7f31cfc7ae"],"state_sha256":"8a84e240573282197decdb960a6721779bfb4e0e52c5f4ad80871093ea35ca92"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"txVtybtERVpYE84bQQd1SaAumOeVXLnOeaj0IHOErE71JMNpzTEIRft6+39XRjNneUBMl80PD1YpgA/Du57qCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T21:16:59.231093Z","bundle_sha256":"d822682a51344bf2422c15623f92f0a0ef51dcdbde1010f151e32b780d5919a9"}}