{"id":"W1556059795","doi":"","title":"A Bayesian Kernel logistic discriminant model: an improvement to the Kernel Fisher's discriminant","year":2008,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; University of Windsor","funders":"","keywords":"Kernel Fisher discriminant analysis; Linear discriminant analysis; Kernel (algebra); Fisher kernel; Pattern recognition (psychology); Artificial intelligence; Discriminant; Kernel method; Mathematics; Optimal discriminant analysis; Bayesian probability; Covariance matrix; Covariance; Computer science; Statistics; Machine learning; Support vector machine; 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":[0.0004678996,0.0002702414,0.0001990704,0.0001628999,0.0006222294,0.0002977448,0.001358768,0.00009173021,0.00009909505],"category_scores_gemma":[0.0002838844,0.0001877916,0.00008835102,0.0003645331,0.0001555074,0.0005702146,0.0002412763,0.000278078,0.0005401305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001219228,"about_ca_system_score_gemma":0.0003849618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001822533,"about_ca_topic_score_gemma":0.0003585172,"domain_scores_codex":[0.9972363,0.00009631396,0.0005113223,0.0007490531,0.0009656422,0.0004413502],"domain_scores_gemma":[0.9983409,0.000124404,0.000137653,0.0005594829,0.0005888845,0.0002486783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001149582,0.0007222793,0.00002215452,0.00001241627,0.00001507988,0.00002396676,0.005017184,0.04788699,0.00906922,0.7128385,0.002705752,0.2215715],"study_design_scores_gemma":[0.0000296875,0.0003282005,0.0003258006,0.00004717769,0.000005312403,0.00001033072,0.000339019,0.8228869,0.01630032,0.1591193,0.0003249277,0.0002829771],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01527519,0.00000751333,0.9704312,0.01060664,0.0004187622,0.0004450332,0.00003809955,0.0001115469,0.002666031],"genre_scores_gemma":[0.9895046,0.00003022174,0.007135421,0.002554632,0.0001331446,0.0001623555,0.00002387714,0.00001398088,0.0004417585],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9742294,"threshold_uncertainty_score":0.7657919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2148833050964134,"score_gpt":0.3546056745877623,"score_spread":0.1397223694913489,"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."}}