{"id":"W2170406903","doi":"10.5555/1390681.1390699","title":"An Information Criterion for Variable Selection in Support Vector Machines","year":2008,"lang":"en","type":"article","venue":"Lirias (KU Leuven)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Support vector machine; Benchmark (surveying); Generalization; Curse of dimensionality; Computer science; Dimension (graph theory); Variable (mathematics); Feature selection; Dimensionality reduction; Relevance vector machine; Selection (genetic algorithm); Data mining; Artificial intelligence; Machine learning; Mathematics","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":[0.01630268,0.001448195,0.002478417,0.004320181,0.0009201369,0.002912703,0.001903471,0.003360296,0.001530799],"category_scores_gemma":[0.06641363,0.0005625212,0.001265168,0.003564239,0.002295769,0.003824233,0.00217913,0.002802367,0.0007952201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001304017,"about_ca_system_score_gemma":0.001481789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009968095,"about_ca_topic_score_gemma":0.0007284043,"domain_scores_codex":[0.9846986,0.007614851,0.001210296,0.001026713,0.004991961,0.0004576562],"domain_scores_gemma":[0.9571314,0.03311205,0.001775892,0.001682489,0.005817204,0.0004809957],"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":[0.0005445089,0.0002658396,0.00609129,0.001014754,0.000446881,0.0004957479,0.0002633155,0.4738373,0.008195116,0.1167229,0.01062935,0.3814931],"study_design_scores_gemma":[0.00005885195,0.000280303,0.001259816,0.0001348787,0.00004654045,0.0001451934,0.0000268743,0.9394046,0.003177263,0.05331303,0.002084085,0.00006860453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007617811,0.001171417,0.9893232,0.0003869424,0.0001012506,0.0001248977,0.0001288179,0.0002290351,0.0009166218],"genre_scores_gemma":[0.3627923,0.001474651,0.6304433,0.0005918968,0.001020388,0.0009054227,0.00117769,0.0002110316,0.001383312],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01630268,"threshold_uncertainty_score":0.08621788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01761514711537405,"score_gpt":0.258854711396319,"score_spread":0.241239564280945,"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."}}