{"id":"W2091462060","doi":"10.1109/icassp.2013.6638325","title":"Metric based Gaussian kernel learning for classification","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mahalanobis distance; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature vector; Metric (unit); k-nearest neighbors algorithm; Support vector machine; Gaussian; Machine learning; Metric space; Parametric statistics; Mathematics; Statistics","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":[0.000112903,0.00005424692,0.00005418939,0.0001288947,0.0001005512,0.0001484505,0.0002066768,0.00003968269,0.0002182725],"category_scores_gemma":[0.00006970699,0.00004213062,0.00003805144,0.0002842885,0.000006833736,0.0004326748,0.00002376005,0.00005127167,0.000624383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001299158,"about_ca_system_score_gemma":0.00001919476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002787361,"about_ca_topic_score_gemma":9.179716e-7,"domain_scores_codex":[0.9994452,0.00002657946,0.0001023477,0.0001871684,0.0001049695,0.0001337284],"domain_scores_gemma":[0.999547,0.000107669,0.00004551667,0.0001589654,0.00008614244,0.00005471015],"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":[0.000005971054,0.0001398609,0.002481238,0.00004223014,0.00001125414,4.539993e-7,0.0001625111,0.0005449821,0.05592449,0.02295039,0.06848817,0.8492484],"study_design_scores_gemma":[0.0002381619,0.00004560947,0.01004555,0.000008065511,0.000001742983,3.714241e-7,0.00004932954,0.9691879,0.009540363,0.001763696,0.009030322,0.00008892979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003898969,0.000009513816,0.9841582,0.002462917,0.00009894053,0.0002130047,1.642636e-7,0.0001739274,0.00898435],"genre_scores_gemma":[0.8930845,0.000002008675,0.1038539,0.0004978832,0.00002380875,0.0001327057,0.000008843055,0.000004229682,0.002392168],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9686429,"threshold_uncertainty_score":0.8025389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03344707675680329,"score_gpt":0.2615691564160104,"score_spread":0.2281220796592071,"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."}}