{"id":"W2603242634","doi":"10.1109/tnnls.2017.2676101","title":"Logistic Localized Modeling of the Sample Space for Feature Selection and Classification","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McMaster University","funders":"","keywords":"Pattern recognition (psychology); Discriminative model; Feature selection; Sample space; Feature vector; Disjoint sets; Feature (linguistics); Artificial intelligence; Mathematics; Computer science; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001757451,0.001102266,0.001064502,0.0009143968,0.0004207368,0.00102703,0.001656673,0.0007816759,0.002232017],"category_scores_gemma":[0.003900734,0.0004606046,0.001218004,0.001864625,0.001014618,0.00151028,0.001338044,0.001940115,0.001132207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009179823,"about_ca_system_score_gemma":0.0008216266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002807984,"about_ca_topic_score_gemma":0.002201172,"domain_scores_codex":[0.9985561,0.0005601138,0.00005904839,0.0003026259,0.0004335019,0.00008858855],"domain_scores_gemma":[0.9986848,0.0007422907,0.0001552922,0.0001655398,0.0002152135,0.00003686745],"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":[0.0002482786,0.00009729437,0.001906231,0.000211773,0.0001022678,0.0002103838,0.0001920048,0.748172,0.01212247,0.04136566,0.002897467,0.1924741],"study_design_scores_gemma":[0.00000485105,0.00003210067,0.0001584985,0.000004909177,0.000005132743,0.00002424657,0.000008187415,0.9926527,0.0008622299,0.0051732,0.001065767,0.000008172718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002850943,0.0001487551,0.9964946,0.00006495695,0.000008509885,0.00002573834,0.00002482506,0.0001177079,0.0002639721],"genre_scores_gemma":[0.4319936,0.001019182,0.5598564,0.0001757431,0.0001435123,0.0008405019,0.0006303109,0.0001717113,0.005168958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002807984,"threshold_uncertainty_score":0.00929445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04827772332104714,"score_gpt":0.2738861454106201,"score_spread":0.2256084220895729,"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."}}