{"id":"W2948950091","doi":"10.48550/arxiv.1906.02590","title":"Linear and Quadratic Discriminant Analysis: Tutorial","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quadratic classifier; Linear discriminant analysis; Kernel Fisher discriminant analysis; Mathematics; Optimal discriminant analysis; Mahalanobis distance; Artificial intelligence; Pattern recognition (psychology); Principal component analysis; Bayes' theorem; Naive Bayes classifier; Multiple discriminant analysis; Binary classification; Machine learning; Statistics; Classifier (UML); Bayesian probability; Computer science; Support vector machine","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":[0.000154225,0.0002193602,0.0003532096,0.0003325881,0.000101308,0.0001334833,0.0007388418,0.0002267266,0.00002433503],"category_scores_gemma":[0.00002100913,0.00021416,0.0002062015,0.0005320939,0.00005082019,0.0003448678,0.001341665,0.0003309116,0.0001312617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004941453,"about_ca_system_score_gemma":0.00009851738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002154672,"about_ca_topic_score_gemma":0.00004740102,"domain_scores_codex":[0.998531,0.0001106992,0.0001575041,0.0008917253,0.00008826105,0.0002207753],"domain_scores_gemma":[0.9986887,0.00007447541,0.0001687726,0.0008564962,0.00008870322,0.0001228619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002583397,0.0006364716,0.02791115,0.0008696453,0.003028526,0.0009660794,0.004258951,0.7828712,0.0009024441,0.1695826,0.004121551,0.004592961],"study_design_scores_gemma":[0.0004030413,0.00004919657,0.002298674,0.00009331721,0.0006055336,0.000001465077,0.0001230581,0.9826459,0.0001690616,0.01275821,0.0004473864,0.0004051856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.469444,0.00005061488,0.5282928,0.00008902674,0.001113339,0.0001889103,0.00001274324,0.0001116846,0.000696895],"genre_scores_gemma":[0.9966288,0.0001621221,0.001424489,0.00005387655,0.0001108412,6.879754e-7,0.00002951199,0.000007337183,0.001582293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5271848,"threshold_uncertainty_score":0.8733193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06530002424121407,"score_gpt":0.1932816535463205,"score_spread":0.1279816293051065,"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."}}