{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006819057,0.001814596,0.001784612,0.001753025,0.000553683,0.003172431,0.001900687,0.00296212,0.001593975],"category_scores_gemma":[0.02360201,0.0006448904,0.001449372,0.001497014,0.003436477,0.004671246,0.002418455,0.003691962,0.0004912742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001261865,"about_ca_system_score_gemma":0.0007646143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005982501,"about_ca_topic_score_gemma":0.0003862751,"domain_scores_codex":[0.9942808,0.002444531,0.0003324237,0.0007887247,0.001896905,0.0002565447],"domain_scores_gemma":[0.9908709,0.006030923,0.001235535,0.0008444035,0.0008236065,0.0001945749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006596535,0.00007205896,0.0007072786,0.0002459441,0.000126036,0.0001582531,0.0001960417,0.3553126,0.003809588,0.574393,0.002099487,0.06281377],"study_design_scores_gemma":[0.00001137808,0.00008384644,0.0002196765,0.00004237367,0.00001896174,0.0000819421,0.00002150018,0.6820434,0.001454147,0.3141426,0.00185272,0.0000274673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003443645,0.0005910837,0.9939221,0.0007044037,0.00004266398,0.00001999346,0.0000309078,0.00004855514,0.001196648],"genre_scores_gemma":[0.491488,0.002618614,0.497685,0.0009066992,0.001184232,0.0004494385,0.0002574286,0.0001902278,0.005220374],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006819057,"threshold_uncertainty_score":0.03606302,"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."}}