{"id":"W2112810953","doi":"10.1016/j.patcog.2011.01.009","title":"From classifiers to discriminators: A nearest neighbor rule induced discriminant analysis","year":2011,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Concordia University","keywords":"Pattern recognition (psychology); Linear discriminant analysis; Artificial intelligence; Discriminant; Kernel Fisher discriminant analysis; Classifier (UML); Optimal discriminant analysis; k-nearest neighbors algorithm; Discriminator; Computer science; Multiple discriminant analysis; Feature extraction; Quadratic classifier; Mathematics; Facial recognition system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002700824,0.000590778,0.001472224,0.0009864968,0.0006170762,0.001602054,0.001500724,0.0008671824,0.002224172],"category_scores_gemma":[0.006480448,0.0004538835,0.0008535994,0.0008947555,0.000854033,0.001794511,0.001485786,0.002700774,0.00136382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004010987,"about_ca_system_score_gemma":0.0009235506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001520302,"about_ca_topic_score_gemma":0.001655398,"domain_scores_codex":[0.9978642,0.0007414664,0.0001124193,0.0004944555,0.0006703793,0.0001170649],"domain_scores_gemma":[0.9981189,0.0007674553,0.00007582461,0.0003765119,0.0005816434,0.00007965563],"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":[0.0003679865,0.0002487856,0.002020319,0.0001466516,0.0001145296,0.00006146997,0.0001715865,0.03119291,0.01278405,0.02046199,0.005959344,0.9264703],"study_design_scores_gemma":[0.00004898825,0.0002003731,0.001571088,0.00004151743,0.00006778041,0.000131493,0.00009056866,0.943216,0.007847657,0.04206079,0.004677145,0.00004662057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03206608,0.0008062917,0.9636802,0.0003124235,0.0001698339,0.0001037271,0.00015034,0.0006939098,0.002017125],"genre_scores_gemma":[0.4731203,0.000695857,0.517626,0.0002495404,0.0001770366,0.0001872699,0.0008152891,0.0003184004,0.006810242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002700824,"threshold_uncertainty_score":0.01428348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08822907585514603,"score_gpt":0.2669186533875215,"score_spread":0.1786895775323754,"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."}}