{"id":"W2158019970","doi":"","title":"K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms","year":2001,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Hyperplane; k-nearest neighbors algorithm; Best bin first; Support vector machine; Tangent; Intuition; Margin (machine learning); Regular polygon; Artificial intelligence; Computer science; Algorithm; Tangent space; Pattern recognition (psychology); Nearest neighbor search; Mathematics; Machine learning; Combinatorics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005496275,0.00007531013,0.00008361915,0.00002623749,0.00008074568,0.000105251,0.0001787865,0.000039231,0.0001210774],"category_scores_gemma":[0.000006608758,0.00005837752,0.00001508454,0.0001175054,0.00004756299,0.0003285343,0.00008204446,0.00006118519,0.0001754753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006916215,"about_ca_system_score_gemma":0.0000120129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005326419,"about_ca_topic_score_gemma":0.0000191335,"domain_scores_codex":[0.9993773,0.0000161631,0.00009243997,0.0002272574,0.0001241085,0.0001627989],"domain_scores_gemma":[0.9996322,0.00004281372,0.00001997961,0.00018095,0.00002780293,0.00009626563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002903637,0.0001217381,0.005299708,0.00001818729,0.00001359095,0.0001950692,0.0003484393,0.00003181862,0.001864265,0.02113922,0.03399101,0.9369479],"study_design_scores_gemma":[0.001226317,0.0002026937,0.01247062,0.00006679011,0.000006723465,0.0003217493,0.0002508784,0.5539493,0.006298408,0.005230975,0.4194258,0.0005497094],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01819477,0.0001716178,0.9691687,0.001820957,0.0001740423,0.00006404587,0.000002500398,0.0001402664,0.01026313],"genre_scores_gemma":[0.9739665,0.0002481112,0.02098679,0.001429558,0.00005500723,0.000009576615,0.000007687232,0.000005927923,0.003290873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9557717,"threshold_uncertainty_score":0.2380566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123374620081723,"score_gpt":0.228698276451739,"score_spread":0.2163608144435667,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}