{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001608818,0.0002473996,0.0002923522,0.0004688565,0.0001575024,0.0001978316,0.0005622988,0.0001311364,0.0005619584],"category_scores_gemma":[0.00005111797,0.0002177817,0.0002040875,0.0008706877,0.00002369189,0.0008071527,0.0002326271,0.0001812433,0.001600208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005436733,"about_ca_system_score_gemma":0.00002827387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002968101,"about_ca_topic_score_gemma":0.0003511935,"domain_scores_codex":[0.9980249,0.0001391269,0.0003652716,0.0007278183,0.0003402879,0.0004025975],"domain_scores_gemma":[0.9987829,0.00006509417,0.0001492924,0.000576051,0.0001300076,0.0002966662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004519853,0.0003403867,0.01061624,0.00001906864,0.00025379,0.00005545474,0.01040189,0.000001605489,0.01001615,0.00002508685,0.001093735,0.9671314],"study_design_scores_gemma":[0.001681071,0.0006712461,0.7046069,0.0005580609,0.001382314,0.00001183823,0.004622179,0.01978961,0.235025,0.02880483,0.0006062353,0.002240649],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6835403,0.000007041263,0.3137015,0.0003606252,0.0004611287,0.0001992187,0.00009020598,0.0001572032,0.001482766],"genre_scores_gemma":[0.988333,0.000008340578,0.01009118,0.0009720524,0.0001339792,0.0001367722,0.0002720997,0.00001922453,0.00003338751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9648907,"threshold_uncertainty_score":0.9991772,"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."}}