{"id":"W4390919463","doi":"10.1007/s00180-023-01448-z","title":"High-dimensional penalized Bernstein support vector classifier","year":2024,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Control Systems and Identification","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Hinge loss; Support vector machine; Algorithm; Mathematics; Estimator; Coordinate descent; Upper and lower bounds; Mathematical optimization; Computer science; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001062895,0.00011876,0.0001369195,0.00008262091,0.00005654529,0.000146618,0.00006534783,0.00004468566,0.0007514003],"category_scores_gemma":[0.00002218993,0.0001201881,0.00003785399,0.000121806,0.00002419645,0.00009135396,0.00001331044,0.0001021388,0.0008448794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009773819,"about_ca_system_score_gemma":0.00006523562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002451772,"about_ca_topic_score_gemma":0.00001418185,"domain_scores_codex":[0.999078,0.00001978016,0.0002985056,0.0001681563,0.0002929598,0.000142542],"domain_scores_gemma":[0.9995159,0.0002101169,0.00002104434,0.00008386889,0.0001018628,0.00006723156],"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":[0.00001111125,0.00002553646,0.00003804165,0.0002549739,0.0001744573,0.00007855189,0.0001508366,0.3683306,0.001616342,0.2577745,0.3462426,0.02530245],"study_design_scores_gemma":[0.0002721051,0.00001939346,0.006771662,0.00003578879,0.00002893043,0.00001983582,0.000004304145,0.9398128,0.00004293244,0.01758388,0.0352364,0.0001720062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01348248,0.0005473403,0.9803409,0.0001848675,0.0029601,0.0001947113,0.001043092,0.0004454888,0.0008010386],"genre_scores_gemma":[0.9787308,0.000005081301,0.01815047,0.00003418787,0.000294327,0.00002256771,0.00109916,0.00003442874,0.001628989],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9652483,"threshold_uncertainty_score":0.9999331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075870931033427,"score_gpt":0.2312339420742583,"score_spread":0.220475232763924,"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."}}