{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ICITOWX7HXCZFOGDWBQXTV753Z","short_pith_number":"pith:ICITOWX7","schema_version":"1.0","canonical_sha256":"4091375aff3dc592b8c3b06179d7fdde74b7bbeb042cd06f6fb234de3d59798f","source":{"kind":"arxiv","id":"1906.09443","version":1},"attestation_state":"computed","paper":{"title":"An enhanced KNN-based twin support vector machine with stable learning rules","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"A. Mir, Jalal A. Nasiri","submitted_at":"2019-06-22T13:03:01Z","abstract_excerpt":"Among the extensions of twin support vector machine (TSVM), some scholars have utilized K-nearest neighbor (KNN) graph to enhance TSVM's classification accuracy. However, these KNN-based TSVM classifiers have two major issues such as high computational cost and overfitting. In order to address these issues, this paper presents an enhanced regularized K-nearest neighbor based twin support vector machine (RKNN-TSVM). It has three additional advantages: (1) Weight is given to each sample by considering the distance from its nearest neighbors. This further reduces the effect of noise and outliers "},"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":"1906.09443","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-22T13:03:01Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"1904930587ce95eb8296846284b54ea940de437a039fe7ac02a865906dc1e90d","abstract_canon_sha256":"b328ed76c191e79c41234db81f0ac0f50f9945da650547e9658695f1816bdfef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:38.032138Z","signature_b64":"TExtOFkhSeauIuc/90X0ulKR66dqN01nOGLrQSOfjTbql08RJPEW5Jzudnv4z7qPDF0vFCjPU87QwWFfwoZkCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4091375aff3dc592b8c3b06179d7fdde74b7bbeb042cd06f6fb234de3d59798f","last_reissued_at":"2026-05-17T23:42:38.031428Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:38.031428Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An enhanced KNN-based twin support vector machine with stable learning rules","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"A. Mir, Jalal A. Nasiri","submitted_at":"2019-06-22T13:03:01Z","abstract_excerpt":"Among the extensions of twin support vector machine (TSVM), some scholars have utilized K-nearest neighbor (KNN) graph to enhance TSVM's classification accuracy. However, these KNN-based TSVM classifiers have two major issues such as high computational cost and overfitting. In order to address these issues, this paper presents an enhanced regularized K-nearest neighbor based twin support vector machine (RKNN-TSVM). It has three additional advantages: (1) Weight is given to each sample by considering the distance from its nearest neighbors. This further reduces the effect of noise and outliers "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.09443","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":"1906.09443","created_at":"2026-05-17T23:42:38.031534+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.09443v1","created_at":"2026-05-17T23:42:38.031534+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.09443","created_at":"2026-05-17T23:42:38.031534+00:00"},{"alias_kind":"pith_short_12","alias_value":"ICITOWX7HXCZ","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"ICITOWX7HXCZFOGD","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"ICITOWX7","created_at":"2026-05-18T12:33:18.533446+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/ICITOWX7HXCZFOGDWBQXTV753Z","json":"https://pith.science/pith/ICITOWX7HXCZFOGDWBQXTV753Z.json","graph_json":"https://pith.science/api/pith-number/ICITOWX7HXCZFOGDWBQXTV753Z/graph.json","events_json":"https://pith.science/api/pith-number/ICITOWX7HXCZFOGDWBQXTV753Z/events.json","paper":"https://pith.science/paper/ICITOWX7"},"agent_actions":{"view_html":"https://pith.science/pith/ICITOWX7HXCZFOGDWBQXTV753Z","download_json":"https://pith.science/pith/ICITOWX7HXCZFOGDWBQXTV753Z.json","view_paper":"https://pith.science/paper/ICITOWX7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.09443&json=true","fetch_graph":"https://pith.science/api/pith-number/ICITOWX7HXCZFOGDWBQXTV753Z/graph.json","fetch_events":"https://pith.science/api/pith-number/ICITOWX7HXCZFOGDWBQXTV753Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ICITOWX7HXCZFOGDWBQXTV753Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ICITOWX7HXCZFOGDWBQXTV753Z/action/storage_attestation","attest_author":"https://pith.science/pith/ICITOWX7HXCZFOGDWBQXTV753Z/action/author_attestation","sign_citation":"https://pith.science/pith/ICITOWX7HXCZFOGDWBQXTV753Z/action/citation_signature","submit_replication":"https://pith.science/pith/ICITOWX7HXCZFOGDWBQXTV753Z/action/replication_record"}},"created_at":"2026-05-17T23:42:38.031534+00:00","updated_at":"2026-05-17T23:42:38.031534+00:00"}