{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:L6FB4VURFF33G3WXY76KQYMX5R","short_pith_number":"pith:L6FB4VUR","canonical_record":{"source":{"id":"1112.3697","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2011-12-16T01:06:47Z","cross_cats_sorted":[],"title_canon_sha256":"52a262af94e82ba1179b7264e99d63577b1e334fd8d6a365d07b0ff34d28f98d","abstract_canon_sha256":"c18c7ac80bdc5a8bfac02f6fd86ebc445ea66604cb7c541d05b5392de2b1a492"},"schema_version":"1.0"},"canonical_sha256":"5f8a1e56912977b36ed7c7fca86197ec793c1a7b598b9dd76a4ae17a082bf414","source":{"kind":"arxiv","id":"1112.3697","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1112.3697","created_at":"2026-05-18T03:40:14Z"},{"alias_kind":"arxiv_version","alias_value":"1112.3697v1","created_at":"2026-05-18T03:40:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1112.3697","created_at":"2026-05-18T03:40:14Z"},{"alias_kind":"pith_short_12","alias_value":"L6FB4VURFF33","created_at":"2026-05-18T12:26:34Z"},{"alias_kind":"pith_short_16","alias_value":"L6FB4VURFF33G3WX","created_at":"2026-05-18T12:26:34Z"},{"alias_kind":"pith_short_8","alias_value":"L6FB4VUR","created_at":"2026-05-18T12:26:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:L6FB4VURFF33G3WXY76KQYMX5R","target":"record","payload":{"canonical_record":{"source":{"id":"1112.3697","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2011-12-16T01:06:47Z","cross_cats_sorted":[],"title_canon_sha256":"52a262af94e82ba1179b7264e99d63577b1e334fd8d6a365d07b0ff34d28f98d","abstract_canon_sha256":"c18c7ac80bdc5a8bfac02f6fd86ebc445ea66604cb7c541d05b5392de2b1a492"},"schema_version":"1.0"},"canonical_sha256":"5f8a1e56912977b36ed7c7fca86197ec793c1a7b598b9dd76a4ae17a082bf414","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:40:14.344079Z","signature_b64":"JNckLerOC5QSEvYgqwDyO4L2k2XlpPH3+6d970JHi9N6/PrlZwzxCX7aRndG/O86u/nYvHXJsKMod11ZEO1wCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f8a1e56912977b36ed7c7fca86197ec793c1a7b598b9dd76a4ae17a082bf414","last_reissued_at":"2026-05-18T03:40:14.343272Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:40:14.343272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1112.3697","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":"2026-05-18T03:40:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VD62uTORayZbd1s+W5GNePuakLhuth6+z/W5XjuRebu0cByMUBtGeT/RigdWluabxWMqN/WbBbtSm31/QyZ2Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T22:59:17.046211Z"},"content_sha256":"d1e7e02b7ed189be6db674a69c05f0a7745c4fe2f101c1d04dd56f6eb876f149","schema_version":"1.0","event_id":"sha256:d1e7e02b7ed189be6db674a69c05f0a7745c4fe2f101c1d04dd56f6eb876f149"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:L6FB4VURFF33G3WXY76KQYMX5R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Insights from Classifying Visual Concepts with Multiple Kernel Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Binder, Christina M\\\"uller, Klaus-Robert M\\\"uller, Marius Kloft, Motoaki Kawanabe, Shinichi Nakajima, Ulf Brefeld, Wojciech Samek","submitted_at":"2011-12-16T01:06:47Z","abstract_excerpt":"Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, so-called 1-norm MKL variants are often observed to be outperformed by an unweighted sum kernel. The contribution of this paper is twofold: We apply a recently developed non-sparse MKL var"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1112.3697","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":"2026-05-18T03:40:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JT7dVwhwnBnyU1QBptCjtEJ8WHuFnaCtiwNIYziL2B157q7du9aaNsTMrHpRBKmjxcAXVFHmDLT5CTZr3IVcCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T22:59:17.046675Z"},"content_sha256":"56f6c3d7989faa60383a4c792c662f7d2567c32a41892d6168ed26c78d1e23d7","schema_version":"1.0","event_id":"sha256:56f6c3d7989faa60383a4c792c662f7d2567c32a41892d6168ed26c78d1e23d7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L6FB4VURFF33G3WXY76KQYMX5R/bundle.json","state_url":"https://pith.science/pith/L6FB4VURFF33G3WXY76KQYMX5R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L6FB4VURFF33G3WXY76KQYMX5R/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":"2026-05-23T22:59:17Z","links":{"resolver":"https://pith.science/pith/L6FB4VURFF33G3WXY76KQYMX5R","bundle":"https://pith.science/pith/L6FB4VURFF33G3WXY76KQYMX5R/bundle.json","state":"https://pith.science/pith/L6FB4VURFF33G3WXY76KQYMX5R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L6FB4VURFF33G3WXY76KQYMX5R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:L6FB4VURFF33G3WXY76KQYMX5R","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":"c18c7ac80bdc5a8bfac02f6fd86ebc445ea66604cb7c541d05b5392de2b1a492","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2011-12-16T01:06:47Z","title_canon_sha256":"52a262af94e82ba1179b7264e99d63577b1e334fd8d6a365d07b0ff34d28f98d"},"schema_version":"1.0","source":{"id":"1112.3697","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1112.3697","created_at":"2026-05-18T03:40:14Z"},{"alias_kind":"arxiv_version","alias_value":"1112.3697v1","created_at":"2026-05-18T03:40:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1112.3697","created_at":"2026-05-18T03:40:14Z"},{"alias_kind":"pith_short_12","alias_value":"L6FB4VURFF33","created_at":"2026-05-18T12:26:34Z"},{"alias_kind":"pith_short_16","alias_value":"L6FB4VURFF33G3WX","created_at":"2026-05-18T12:26:34Z"},{"alias_kind":"pith_short_8","alias_value":"L6FB4VUR","created_at":"2026-05-18T12:26:34Z"}],"graph_snapshots":[{"event_id":"sha256:56f6c3d7989faa60383a4c792c662f7d2567c32a41892d6168ed26c78d1e23d7","target":"graph","created_at":"2026-05-18T03:40:14Z","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":"Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, so-called 1-norm MKL variants are often observed to be outperformed by an unweighted sum kernel. The contribution of this paper is twofold: We apply a recently developed non-sparse MKL var","authors_text":"Alexander Binder, Christina M\\\"uller, Klaus-Robert M\\\"uller, Marius Kloft, Motoaki Kawanabe, Shinichi Nakajima, Ulf Brefeld, Wojciech Samek","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2011-12-16T01:06:47Z","title":"Insights from Classifying Visual Concepts with Multiple Kernel Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1112.3697","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:d1e7e02b7ed189be6db674a69c05f0a7745c4fe2f101c1d04dd56f6eb876f149","target":"record","created_at":"2026-05-18T03:40:14Z","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":"c18c7ac80bdc5a8bfac02f6fd86ebc445ea66604cb7c541d05b5392de2b1a492","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2011-12-16T01:06:47Z","title_canon_sha256":"52a262af94e82ba1179b7264e99d63577b1e334fd8d6a365d07b0ff34d28f98d"},"schema_version":"1.0","source":{"id":"1112.3697","kind":"arxiv","version":1}},"canonical_sha256":"5f8a1e56912977b36ed7c7fca86197ec793c1a7b598b9dd76a4ae17a082bf414","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5f8a1e56912977b36ed7c7fca86197ec793c1a7b598b9dd76a4ae17a082bf414","first_computed_at":"2026-05-18T03:40:14.343272Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:40:14.343272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JNckLerOC5QSEvYgqwDyO4L2k2XlpPH3+6d970JHi9N6/PrlZwzxCX7aRndG/O86u/nYvHXJsKMod11ZEO1wCA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:40:14.344079Z","signed_message":"canonical_sha256_bytes"},"source_id":"1112.3697","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d1e7e02b7ed189be6db674a69c05f0a7745c4fe2f101c1d04dd56f6eb876f149","sha256:56f6c3d7989faa60383a4c792c662f7d2567c32a41892d6168ed26c78d1e23d7"],"state_sha256":"7c4bf9a804d029116d209276d92fb8ae5526e997df99d646c6372ce29ae609a0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Eea7o9NQMKn6yXeOecomIcHNIS+O+0BBSvolZgjp7TRphqsq7yEJQkmTgcRIkU/qTyUa3HLpKuBYQsuBp8/7DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T22:59:17.049687Z","bundle_sha256":"999fb3a97bc14caef58f3552077d23c0b203af0ee851a38dd70d3b9429e2ca5d"}}