{"id":"W1633303","doi":"","title":"Robustness, Risk, and Regularization in Support Vector Machines","year":2008,"lang":"en","type":"article","venue":"Targeted diagnosis and therapy","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Support vector machine; Robustness (evolution); Overfitting; Mathematical optimization; Regular polygon; Computer science; Margin classifier; Mathematics; Regularization perspectives on support vector machines; Regularization (linguistics); Artificial intelligence; Convex optimization; Machine learning; Algorithm; Inverse problem; Tikhonov regularization; Artificial neural network","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":[0.00005175205,0.0001112778,0.0001387,0.00007715439,0.00006289731,0.00001614759,0.00003568597,0.00006273968,0.00003444661],"category_scores_gemma":[0.00001024757,0.00009978659,0.00001697792,0.0001069076,0.00003626322,0.0000832388,0.000009621292,0.0000875809,5.881578e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006699032,"about_ca_system_score_gemma":0.000003921919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000716678,"about_ca_topic_score_gemma":0.00001405221,"domain_scores_codex":[0.9995634,0.00002596018,0.0001110451,0.000127173,0.00005538241,0.0001170559],"domain_scores_gemma":[0.9997898,0.00004004193,0.00002040793,0.0001006187,0.00001551163,0.00003359473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005932408,0.0001766589,0.7852674,0.00002355579,0.00009725803,0.00008774851,0.001078735,0.0138322,0.007006825,0.0004623353,0.01328849,0.1786194],"study_design_scores_gemma":[0.001865052,0.0002468591,0.7360535,0.00008472232,0.00001824483,0.00005003098,0.00001964981,0.1358181,0.1024145,0.002434541,0.02027982,0.0007150452],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909616,0.00699487,0.00134287,0.00006130649,0.00006448109,0.0001288326,0.000006771407,0.0002425852,0.0001967346],"genre_scores_gemma":[0.9360847,0.06277097,0.0009575334,0.00006506484,0.0000389165,0.00003253984,0.00001242933,0.00002069011,0.00001709168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1779044,"threshold_uncertainty_score":0.4069179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540895688194992,"score_gpt":0.2152312619610298,"score_spread":0.1998223050790799,"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."}}