{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5RG537ANIWC5KUWR2JKXUTHYH5","short_pith_number":"pith:5RG537AN","schema_version":"1.0","canonical_sha256":"ec4dddfc0d4585d552d1d2557a4cf83f4473a8c44bef6b0cf851af014164e519","source":{"kind":"arxiv","id":"1805.07438","version":1},"attestation_state":"computed","paper":{"title":"Region-Based Classification of PolSAR Data Using Radial Basis Kernel Functions With Stochastic Distances","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"stat.ML","authors_text":"A. C. Frery, L. V. Dutra, R. G. Negri, T. S. G. Mendes, W. B. Silva","submitted_at":"2018-05-07T19:23:07Z","abstract_excerpt":"Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments. Silva et al (2013) used stochastic distances between complex multivariate Wishart models which, differently from other measures, are computationally tractable. In this work we assess the robustness of such approach with respect to errors in the training stage, and propose an extension that alleviates such problems. We introduce robustness in the process by incorporating a combination of radial basis kernel functions and stochastic dist"},"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":"1805.07438","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-07T19:23:07Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"3ea0b08bcbcb897009acb8a7ed1fbf83e9b917430def22fc085e8ec7a8c5b59f","abstract_canon_sha256":"4b96cb1ef75acc0a16e8d8c780884e38c0ac7b77fe2ee054c5835d60bf254986"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:33.587855Z","signature_b64":"Qh1xLXiA7TK66+vvaiDIkczVxyFEYoymWo8IgtkJUDDXGZpzO6eMUPAe9PPpoA1pCwruD2lh5C3cRs5cyikCCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec4dddfc0d4585d552d1d2557a4cf83f4473a8c44bef6b0cf851af014164e519","last_reissued_at":"2026-05-18T00:15:33.587073Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:33.587073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Region-Based Classification of PolSAR Data Using Radial Basis Kernel Functions With Stochastic Distances","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"stat.ML","authors_text":"A. C. Frery, L. V. Dutra, R. G. Negri, T. S. G. Mendes, W. B. Silva","submitted_at":"2018-05-07T19:23:07Z","abstract_excerpt":"Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments. Silva et al (2013) used stochastic distances between complex multivariate Wishart models which, differently from other measures, are computationally tractable. In this work we assess the robustness of such approach with respect to errors in the training stage, and propose an extension that alleviates such problems. We introduce robustness in the process by incorporating a combination of radial basis kernel functions and stochastic dist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07438","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1805.07438","created_at":"2026-05-18T00:15:33.587190+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.07438v1","created_at":"2026-05-18T00:15:33.587190+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07438","created_at":"2026-05-18T00:15:33.587190+00:00"},{"alias_kind":"pith_short_12","alias_value":"5RG537ANIWC5","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5RG537ANIWC5KUWR","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5RG537AN","created_at":"2026-05-18T12:32:08.215937+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/5RG537ANIWC5KUWR2JKXUTHYH5","json":"https://pith.science/pith/5RG537ANIWC5KUWR2JKXUTHYH5.json","graph_json":"https://pith.science/api/pith-number/5RG537ANIWC5KUWR2JKXUTHYH5/graph.json","events_json":"https://pith.science/api/pith-number/5RG537ANIWC5KUWR2JKXUTHYH5/events.json","paper":"https://pith.science/paper/5RG537AN"},"agent_actions":{"view_html":"https://pith.science/pith/5RG537ANIWC5KUWR2JKXUTHYH5","download_json":"https://pith.science/pith/5RG537ANIWC5KUWR2JKXUTHYH5.json","view_paper":"https://pith.science/paper/5RG537AN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.07438&json=true","fetch_graph":"https://pith.science/api/pith-number/5RG537ANIWC5KUWR2JKXUTHYH5/graph.json","fetch_events":"https://pith.science/api/pith-number/5RG537ANIWC5KUWR2JKXUTHYH5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5RG537ANIWC5KUWR2JKXUTHYH5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5RG537ANIWC5KUWR2JKXUTHYH5/action/storage_attestation","attest_author":"https://pith.science/pith/5RG537ANIWC5KUWR2JKXUTHYH5/action/author_attestation","sign_citation":"https://pith.science/pith/5RG537ANIWC5KUWR2JKXUTHYH5/action/citation_signature","submit_replication":"https://pith.science/pith/5RG537ANIWC5KUWR2JKXUTHYH5/action/replication_record"}},"created_at":"2026-05-18T00:15:33.587190+00:00","updated_at":"2026-05-18T00:15:33.587190+00:00"}