{"id":"W7026636757","doi":"","title":"Adapting heterogeneous ensembles with particle swarm optimization for video face recognition","year":2012,"lang":"en","type":"other","venue":"Espace École de technologie supérieure (École de technologie supérieure)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Facial recognition system; Biometrics; Artificial neural network; Matching (statistics); Pattern recognition (psychology); Face (sociological concept); Supervised learning; Fuzzy logic; Hyperparameter; Identification (biology)","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":[0.000934466,0.001050745,0.001147357,0.0004808007,0.0003734271,0.0008106071,0.001091302,0.001077471,0.0009184373],"category_scores_gemma":[0.002342373,0.0004622639,0.0008306804,0.0004041236,0.0005619994,0.0006339653,0.0009673393,0.0009607652,0.0002238977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006517408,"about_ca_system_score_gemma":0.0006709293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007072412,"about_ca_topic_score_gemma":0.003969388,"domain_scores_codex":[0.9997242,0.00008395334,0.00001487389,0.00007023614,0.00006684285,0.00003981796],"domain_scores_gemma":[0.9993635,0.0003543439,0.00006553135,0.00004274996,0.000134777,0.00003921199],"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":[0.00003016644,0.00002701236,0.0003816898,0.00001693598,0.00003218866,0.00002344222,0.00002488733,0.9800624,0.0007695385,0.001517637,0.0003670758,0.01674702],"study_design_scores_gemma":[0.000001751698,0.000005788568,0.00002754018,7.748187e-7,0.000001388101,0.000001282768,0.000001636956,0.999592,0.00005952294,0.0002549917,0.00005219648,0.000001079334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03677351,0.0005164769,0.960066,0.0002153981,0.0001186856,0.00004798541,0.00003523393,0.0002341793,0.00199261],"genre_scores_gemma":[0.8160287,0.000376201,0.1800071,0.0002113744,0.0001158921,0.0002079887,0.0001925754,0.00006204998,0.00279809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007072412,"threshold_uncertainty_score":0.01406252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0273794532222057,"score_gpt":0.264538820908074,"score_spread":0.2371593676858683,"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."}}