{"id":"W4378418057","doi":"10.1016/j.patcog.2023.109720","title":"Generalization capacity of multi-class SVM based on Markovian resampling","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Generalization; Resampling; Support vector machine; Computer science; Class (philosophy); Artificial intelligence; Algorithm; Mathematics; Mathematical optimization","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":[0.0003490178,0.0001363205,0.0001419286,0.0003472279,0.0001119648,0.00006138011,0.0002311677,0.00009935046,0.00007973055],"category_scores_gemma":[0.00009003333,0.0001348622,0.00007934335,0.0005493004,0.00002274997,0.0002906626,0.00005624335,0.0001113851,0.000537932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003284068,"about_ca_system_score_gemma":0.00002118484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007231547,"about_ca_topic_score_gemma":0.00002500568,"domain_scores_codex":[0.9986564,0.0001458744,0.0002830331,0.0003623753,0.0003192524,0.0002330855],"domain_scores_gemma":[0.9991788,0.0001251194,0.0001581197,0.000306968,0.0001610411,0.00006991978],"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":[0.00003691748,0.000324026,0.0053614,0.0002284198,0.0000209093,0.00001535265,0.000554227,0.005580638,0.04735195,0.00003601831,0.003090317,0.9373998],"study_design_scores_gemma":[0.0007612024,0.00009223592,0.009134911,0.0003045673,0.000008044572,0.000001666383,0.00002619191,0.8855128,0.1028844,0.0008074265,0.0002440471,0.0002225834],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3482307,0.000003055325,0.6499247,0.0004516074,0.0004300285,0.0001997441,0.00006284747,0.0002905572,0.0004067222],"genre_scores_gemma":[0.9885476,0.0000205365,0.009955048,0.0008539521,0.00007511886,0.00005719424,0.0004239617,0.00001763751,0.00004899273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9371772,"threshold_uncertainty_score":0.6914207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08778870370375463,"score_gpt":0.2774680876862693,"score_spread":0.1896793839825147,"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."}}