{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:OXXHXY7RJYONLI2XQT6KHJBIMN","short_pith_number":"pith:OXXHXY7R","schema_version":"1.0","canonical_sha256":"75ee7be3f14e1cd5a35784fca3a428636070835f2e24f81ba90bc145e56963ba","source":{"kind":"arxiv","id":"1403.7308","version":2},"attestation_state":"computed","paper":{"title":"Data Generators for Learning Systems Based on RBF Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Marko Robnik-\\v{S}ikonja","submitted_at":"2014-03-28T08:55:21Z","abstract_excerpt":"There are plenty of problems where the data available is scarce and expensive. We propose a generator of semi-artificial data with similar properties to the original data which enables development and testing of different data mining algorithms and optimization of their parameters. The generated data allow a large scale experimentation and simulations without danger of overfitting. The proposed generator is based on RBF networks, which learn sets of Gaussian kernels. These Gaussian kernels can be used in a generative mode to generate new data from the same distributions. To assess quality of t"},"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":"1403.7308","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-03-28T08:55:21Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"d08ae28e2e9cddb4b4615b09b7eea5329af513e37e056bd5a348856c4c6b1d4c","abstract_canon_sha256":"4cc2c9359baa5d31ed06f9b1d4ffec41258d81f40b40a1b3422c032edcf8b383"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:20:07.708598Z","signature_b64":"SwSQ8BPNXLxekgtY0fGKWt539s8/PgqBvyVb3V6sZ7+mKIvTBupjoSRirfzOhN7iAPkcmLKBcy3JQSUIHBf4BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75ee7be3f14e1cd5a35784fca3a428636070835f2e24f81ba90bc145e56963ba","last_reissued_at":"2026-07-05T01:20:07.708179Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:20:07.708179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data Generators for Learning Systems Based on RBF Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Marko Robnik-\\v{S}ikonja","submitted_at":"2014-03-28T08:55:21Z","abstract_excerpt":"There are plenty of problems where the data available is scarce and expensive. We propose a generator of semi-artificial data with similar properties to the original data which enables development and testing of different data mining algorithms and optimization of their parameters. The generated data allow a large scale experimentation and simulations without danger of overfitting. The proposed generator is based on RBF networks, which learn sets of Gaussian kernels. These Gaussian kernels can be used in a generative mode to generate new data from the same distributions. To assess quality of t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.7308","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1403.7308/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1403.7308","created_at":"2026-07-05T01:20:07.708239+00:00"},{"alias_kind":"arxiv_version","alias_value":"1403.7308v2","created_at":"2026-07-05T01:20:07.708239+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.7308","created_at":"2026-07-05T01:20:07.708239+00:00"},{"alias_kind":"pith_short_12","alias_value":"OXXHXY7RJYON","created_at":"2026-07-05T01:20:07.708239+00:00"},{"alias_kind":"pith_short_16","alias_value":"OXXHXY7RJYONLI2X","created_at":"2026-07-05T01:20:07.708239+00:00"},{"alias_kind":"pith_short_8","alias_value":"OXXHXY7R","created_at":"2026-07-05T01:20:07.708239+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OXXHXY7RJYONLI2XQT6KHJBIMN","json":"https://pith.science/pith/OXXHXY7RJYONLI2XQT6KHJBIMN.json","graph_json":"https://pith.science/api/pith-number/OXXHXY7RJYONLI2XQT6KHJBIMN/graph.json","events_json":"https://pith.science/api/pith-number/OXXHXY7RJYONLI2XQT6KHJBIMN/events.json","paper":"https://pith.science/paper/OXXHXY7R"},"agent_actions":{"view_html":"https://pith.science/pith/OXXHXY7RJYONLI2XQT6KHJBIMN","download_json":"https://pith.science/pith/OXXHXY7RJYONLI2XQT6KHJBIMN.json","view_paper":"https://pith.science/paper/OXXHXY7R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1403.7308&json=true","fetch_graph":"https://pith.science/api/pith-number/OXXHXY7RJYONLI2XQT6KHJBIMN/graph.json","fetch_events":"https://pith.science/api/pith-number/OXXHXY7RJYONLI2XQT6KHJBIMN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OXXHXY7RJYONLI2XQT6KHJBIMN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OXXHXY7RJYONLI2XQT6KHJBIMN/action/storage_attestation","attest_author":"https://pith.science/pith/OXXHXY7RJYONLI2XQT6KHJBIMN/action/author_attestation","sign_citation":"https://pith.science/pith/OXXHXY7RJYONLI2XQT6KHJBIMN/action/citation_signature","submit_replication":"https://pith.science/pith/OXXHXY7RJYONLI2XQT6KHJBIMN/action/replication_record"}},"created_at":"2026-07-05T01:20:07.708239+00:00","updated_at":"2026-07-05T01:20:07.708239+00:00"}