{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:KRQYPXJ6QQD3W2W5PLD63N27JX","short_pith_number":"pith:KRQYPXJ6","canonical_record":{"source":{"id":"1903.03364","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-08T10:51:03Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c5c43d65f7d1d79c5b51e024be36aa2329824ff267e28977b48440786ddaec58","abstract_canon_sha256":"ba8836d0b295fd89c22e12b4b605027e508576ec5a8916009794936597918ef8"},"schema_version":"1.0"},"canonical_sha256":"546187dd3e8407bb6add7ac7edb75f4dfe76d49467bc30691a1d12011fa58c76","source":{"kind":"arxiv","id":"1903.03364","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.03364","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"arxiv_version","alias_value":"1903.03364v2","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.03364","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"pith_short_12","alias_value":"KRQYPXJ6QQD3","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"KRQYPXJ6QQD3W2W5","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"KRQYPXJ6","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:KRQYPXJ6QQD3W2W5PLD63N27JX","target":"record","payload":{"canonical_record":{"source":{"id":"1903.03364","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-08T10:51:03Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c5c43d65f7d1d79c5b51e024be36aa2329824ff267e28977b48440786ddaec58","abstract_canon_sha256":"ba8836d0b295fd89c22e12b4b605027e508576ec5a8916009794936597918ef8"},"schema_version":"1.0"},"canonical_sha256":"546187dd3e8407bb6add7ac7edb75f4dfe76d49467bc30691a1d12011fa58c76","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:25.544547Z","signature_b64":"EtDfwp9HKd3Z6wSpupzgq22ENa+/A5ItSNEzoz3Ergn0z7VjEL8yZLCAPHaAqBX7nSOTVODz1M6n5RyI/RnDBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"546187dd3e8407bb6add7ac7edb75f4dfe76d49467bc30691a1d12011fa58c76","last_reissued_at":"2026-05-17T23:51:25.544007Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:25.544007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.03364","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:51:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xO7+PpNWGhuUY1KgJJ3+6ZbSZa/oj6l9ael7ecUMQCXDnigptWUUhJy0XctMqQrRh4vWpCIG8JX5St+NwGJpBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T11:33:34.998584Z"},"content_sha256":"2ae6ac8fd656917b7f2f4244cf46ed8eb047d1246916619a92d52ce60a175bae","schema_version":"1.0","event_id":"sha256:2ae6ac8fd656917b7f2f4244cf46ed8eb047d1246916619a92d52ce60a175bae"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:KRQYPXJ6QQD3W2W5PLD63N27JX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Babak Hosseini, Barbara Hammer","submitted_at":"2019-03-08T10:51:03Z","abstract_excerpt":"Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space. Focusing on discriminative tasks, MKL has been used successfully for feature selection and finding the significant modalities of the data. In such applications, each base kernel represents one dimension of the data or is derived from one specific descriptor. Therefore, MKL finds an optimal weighting scheme for the given kernels to increase the classification accuracy. Nevertheless, the majority of the works in this area focus on only binary classification pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.03364","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:51:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vm5m2crTD9E/0PWxLVFn6P2kjQnCNhyVeABZJGdxuxMraiP8wXuSWAlOMAiuExYufAA5os3bLBN7VaWIpTu7Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T11:33:34.999234Z"},"content_sha256":"5ee10fbabc9eb4da3a2479b07163d6f676c7d0a894005c9fa2beb8a28a64f2aa","schema_version":"1.0","event_id":"sha256:5ee10fbabc9eb4da3a2479b07163d6f676c7d0a894005c9fa2beb8a28a64f2aa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KRQYPXJ6QQD3W2W5PLD63N27JX/bundle.json","state_url":"https://pith.science/pith/KRQYPXJ6QQD3W2W5PLD63N27JX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KRQYPXJ6QQD3W2W5PLD63N27JX/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":"2026-05-30T11:33:35Z","links":{"resolver":"https://pith.science/pith/KRQYPXJ6QQD3W2W5PLD63N27JX","bundle":"https://pith.science/pith/KRQYPXJ6QQD3W2W5PLD63N27JX/bundle.json","state":"https://pith.science/pith/KRQYPXJ6QQD3W2W5PLD63N27JX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KRQYPXJ6QQD3W2W5PLD63N27JX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:KRQYPXJ6QQD3W2W5PLD63N27JX","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":"ba8836d0b295fd89c22e12b4b605027e508576ec5a8916009794936597918ef8","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-08T10:51:03Z","title_canon_sha256":"c5c43d65f7d1d79c5b51e024be36aa2329824ff267e28977b48440786ddaec58"},"schema_version":"1.0","source":{"id":"1903.03364","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.03364","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"arxiv_version","alias_value":"1903.03364v2","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.03364","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"pith_short_12","alias_value":"KRQYPXJ6QQD3","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"KRQYPXJ6QQD3W2W5","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"KRQYPXJ6","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:5ee10fbabc9eb4da3a2479b07163d6f676c7d0a894005c9fa2beb8a28a64f2aa","target":"graph","created_at":"2026-05-17T23:51:25Z","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":"Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space. Focusing on discriminative tasks, MKL has been used successfully for feature selection and finding the significant modalities of the data. In such applications, each base kernel represents one dimension of the data or is derived from one specific descriptor. Therefore, MKL finds an optimal weighting scheme for the given kernels to increase the classification accuracy. Nevertheless, the majority of the works in this area focus on only binary classification pro","authors_text":"Babak Hosseini, Barbara Hammer","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-08T10:51:03Z","title":"Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.03364","kind":"arxiv","version":2},"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":"sha256:2ae6ac8fd656917b7f2f4244cf46ed8eb047d1246916619a92d52ce60a175bae","target":"record","created_at":"2026-05-17T23:51:25Z","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":"ba8836d0b295fd89c22e12b4b605027e508576ec5a8916009794936597918ef8","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-08T10:51:03Z","title_canon_sha256":"c5c43d65f7d1d79c5b51e024be36aa2329824ff267e28977b48440786ddaec58"},"schema_version":"1.0","source":{"id":"1903.03364","kind":"arxiv","version":2}},"canonical_sha256":"546187dd3e8407bb6add7ac7edb75f4dfe76d49467bc30691a1d12011fa58c76","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"546187dd3e8407bb6add7ac7edb75f4dfe76d49467bc30691a1d12011fa58c76","first_computed_at":"2026-05-17T23:51:25.544007Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:25.544007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EtDfwp9HKd3Z6wSpupzgq22ENa+/A5ItSNEzoz3Ergn0z7VjEL8yZLCAPHaAqBX7nSOTVODz1M6n5RyI/RnDBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:25.544547Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.03364","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2ae6ac8fd656917b7f2f4244cf46ed8eb047d1246916619a92d52ce60a175bae","sha256:5ee10fbabc9eb4da3a2479b07163d6f676c7d0a894005c9fa2beb8a28a64f2aa"],"state_sha256":"8b8e9dc017f12d5e208e1a59cf38abbb4e4ae8a67697d193054605b861553d33"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NdBoX5PE4+m3qZXaslugyjXmKadCCBTf6UAy5+kyg39bBMtHWauUVzPJvr2To4HrU8oCocau+KbposIel73ACQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T11:33:35.002482Z","bundle_sha256":"4b9f605f590d542b66ea7050df2eea414bda9174b637023ba9480125f0ddd225"}}