{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:VKV7JMLYL66HDKAGQ34A7SM7B7","short_pith_number":"pith:VKV7JMLY","canonical_record":{"source":{"id":"1907.06258","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-14T18:35:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5ec167ee2c387dbf0cb987ca8b898048fa0b37fd09db9472f894ec7371f76702","abstract_canon_sha256":"71bc5e34a42f87cd675f34eea7994a409a389c369570399bbaae8329a4c660fc"},"schema_version":"1.0"},"canonical_sha256":"aaabf4b1785fbc71a80686f80fc99f0fc86992c17802fb1fb3bdc53094627c71","source":{"kind":"arxiv","id":"1907.06258","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.06258","created_at":"2026-07-05T00:09:26Z"},{"alias_kind":"arxiv_version","alias_value":"1907.06258v3","created_at":"2026-07-05T00:09:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06258","created_at":"2026-07-05T00:09:26Z"},{"alias_kind":"pith_short_12","alias_value":"VKV7JMLYL66H","created_at":"2026-07-05T00:09:26Z"},{"alias_kind":"pith_short_16","alias_value":"VKV7JMLYL66HDKAG","created_at":"2026-07-05T00:09:26Z"},{"alias_kind":"pith_short_8","alias_value":"VKV7JMLY","created_at":"2026-07-05T00:09:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:VKV7JMLYL66HDKAGQ34A7SM7B7","target":"record","payload":{"canonical_record":{"source":{"id":"1907.06258","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-14T18:35:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5ec167ee2c387dbf0cb987ca8b898048fa0b37fd09db9472f894ec7371f76702","abstract_canon_sha256":"71bc5e34a42f87cd675f34eea7994a409a389c369570399bbaae8329a4c660fc"},"schema_version":"1.0"},"canonical_sha256":"aaabf4b1785fbc71a80686f80fc99f0fc86992c17802fb1fb3bdc53094627c71","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:09:26.236153Z","signature_b64":"brgYDx58ANMuQiqVfqv7nGUu5reJyOkoaLQ6dqOXxazu+gWOd1u1jW1ljS6HH9iTpLpSW6xWgtjtQ2WsBdsVAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aaabf4b1785fbc71a80686f80fc99f0fc86992c17802fb1fb3bdc53094627c71","last_reissued_at":"2026-07-05T00:09:26.235670Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:09:26.235670Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.06258","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:09:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"95c0BzG56+k6nlHU+FzEKjwRYhh8VjlK4qsgveLDM+teNYISqOLIipN5IouDKETzC6OLMh3Ezh3sxj8yeEJBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T09:39:07.396131Z"},"content_sha256":"46008e46f1b4e4ade93f8bca6807bb932c18480e8770bc1e4f2e365784bcf08c","schema_version":"1.0","event_id":"sha256:46008e46f1b4e4ade93f8bca6807bb932c18480e8770bc1e4f2e365784bcf08c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:VKV7JMLYL66HDKAGQ34A7SM7B7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving classification performance by feature space transformations and model selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eric S. Tellez, Jose Ortiz-Bejar, Mario Graff","submitted_at":"2019-07-14T18:35:58Z","abstract_excerpt":"Improving the performance of classifiers is the realm of feature mapping, prototype selection, and kernel function transformations; these techniques aim for reducing the complexity, and also, improving the accuracy of models. In particular, our objective is to combine them to transform data's shape into another more convenient distribution; such that some simple algorithms, such as Na\\\"ive Bayes or k-Nearest Neighbors, can produce competitive classifiers. In this paper, we introduce a family of classifiers based on feature mapping and kernel functions, orchestrated by a model selection scheme "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06258","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1907.06258/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T00:09:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nZRmfd8j8QzDYifcRd/1hHf+SU9XWc7KXfY5rG0SXnF4hwOw8V4QGPSjdKHO8rDlOjymGfdj0m+/yzJFjkNZAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T09:39:07.396514Z"},"content_sha256":"fd2b2dc9602a7976a991956e1a7de768fdc3dc8c5964c0940e225d1f7c9cd4b6","schema_version":"1.0","event_id":"sha256:fd2b2dc9602a7976a991956e1a7de768fdc3dc8c5964c0940e225d1f7c9cd4b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VKV7JMLYL66HDKAGQ34A7SM7B7/bundle.json","state_url":"https://pith.science/pith/VKV7JMLYL66HDKAGQ34A7SM7B7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VKV7JMLYL66HDKAGQ34A7SM7B7/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":"2026-07-05T09:39:07Z","links":{"resolver":"https://pith.science/pith/VKV7JMLYL66HDKAGQ34A7SM7B7","bundle":"https://pith.science/pith/VKV7JMLYL66HDKAGQ34A7SM7B7/bundle.json","state":"https://pith.science/pith/VKV7JMLYL66HDKAGQ34A7SM7B7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VKV7JMLYL66HDKAGQ34A7SM7B7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:VKV7JMLYL66HDKAGQ34A7SM7B7","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":"71bc5e34a42f87cd675f34eea7994a409a389c369570399bbaae8329a4c660fc","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-14T18:35:58Z","title_canon_sha256":"5ec167ee2c387dbf0cb987ca8b898048fa0b37fd09db9472f894ec7371f76702"},"schema_version":"1.0","source":{"id":"1907.06258","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.06258","created_at":"2026-07-05T00:09:26Z"},{"alias_kind":"arxiv_version","alias_value":"1907.06258v3","created_at":"2026-07-05T00:09:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06258","created_at":"2026-07-05T00:09:26Z"},{"alias_kind":"pith_short_12","alias_value":"VKV7JMLYL66H","created_at":"2026-07-05T00:09:26Z"},{"alias_kind":"pith_short_16","alias_value":"VKV7JMLYL66HDKAG","created_at":"2026-07-05T00:09:26Z"},{"alias_kind":"pith_short_8","alias_value":"VKV7JMLY","created_at":"2026-07-05T00:09:26Z"}],"graph_snapshots":[{"event_id":"sha256:fd2b2dc9602a7976a991956e1a7de768fdc3dc8c5964c0940e225d1f7c9cd4b6","target":"graph","created_at":"2026-07-05T00:09:26Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1907.06258/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Improving the performance of classifiers is the realm of feature mapping, prototype selection, and kernel function transformations; these techniques aim for reducing the complexity, and also, improving the accuracy of models. In particular, our objective is to combine them to transform data's shape into another more convenient distribution; such that some simple algorithms, such as Na\\\"ive Bayes or k-Nearest Neighbors, can produce competitive classifiers. In this paper, we introduce a family of classifiers based on feature mapping and kernel functions, orchestrated by a model selection scheme ","authors_text":"Eric S. Tellez, Jose Ortiz-Bejar, Mario Graff","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-14T18:35:58Z","title":"Improving classification performance by feature space transformations and model selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06258","kind":"arxiv","version":3},"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":"sha256:46008e46f1b4e4ade93f8bca6807bb932c18480e8770bc1e4f2e365784bcf08c","target":"record","created_at":"2026-07-05T00:09:26Z","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":"71bc5e34a42f87cd675f34eea7994a409a389c369570399bbaae8329a4c660fc","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-14T18:35:58Z","title_canon_sha256":"5ec167ee2c387dbf0cb987ca8b898048fa0b37fd09db9472f894ec7371f76702"},"schema_version":"1.0","source":{"id":"1907.06258","kind":"arxiv","version":3}},"canonical_sha256":"aaabf4b1785fbc71a80686f80fc99f0fc86992c17802fb1fb3bdc53094627c71","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aaabf4b1785fbc71a80686f80fc99f0fc86992c17802fb1fb3bdc53094627c71","first_computed_at":"2026-07-05T00:09:26.235670Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:09:26.235670Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"brgYDx58ANMuQiqVfqv7nGUu5reJyOkoaLQ6dqOXxazu+gWOd1u1jW1ljS6HH9iTpLpSW6xWgtjtQ2WsBdsVAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:09:26.236153Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.06258","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:46008e46f1b4e4ade93f8bca6807bb932c18480e8770bc1e4f2e365784bcf08c","sha256:fd2b2dc9602a7976a991956e1a7de768fdc3dc8c5964c0940e225d1f7c9cd4b6"],"state_sha256":"b4b259244f7ce8faa4ef4471f8ecf357ebfe40112545df036677263108b40c76"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"28cbVGpLNI5wvaPEsVvWK7KgYSoIY7qyRZECS1e1PCsGo9UYUa0uZUtG0wQX9VoTy3Evhv+X+KvuJDZILw1oDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T09:39:07.398476Z","bundle_sha256":"1409284cbdc3273818d780b7eb565b85e1d0cb6d5b7a23f2dc9f178a395d12c0"}}