{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002262306,0.0001042726,0.000119892,0.0001877097,0.0001570525,0.00009888186,0.0002290849,0.00009269612,0.00006724608],"category_scores_gemma":[0.00005224132,0.0001001523,0.00003497339,0.0003379866,0.00001168064,0.003565872,0.00003227495,0.00009177242,0.00009653322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000477968,"about_ca_system_score_gemma":0.00007962697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000136095,"about_ca_topic_score_gemma":0.00002449744,"domain_scores_codex":[0.999141,0.00004686221,0.0002555557,0.0001860637,0.0001553174,0.0002152303],"domain_scores_gemma":[0.9995597,0.00003976349,0.00008033265,0.0001599786,0.00009834014,0.0000619106],"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":[0.001083638,0.002003994,0.02120544,0.0007313831,0.00008006603,0.00004535811,0.03497499,0.01424653,0.180095,0.06488341,0.2173047,0.4633454],"study_design_scores_gemma":[0.00302202,0.001206187,0.02973603,0.00009588071,0.00001156813,0.0001666087,0.00009324469,0.7371708,0.02006122,0.01171729,0.1959746,0.0007445196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.278085,0.00001062136,0.718114,0.0008447405,0.0009212501,0.0004490302,0.00002082106,0.0002861787,0.001268356],"genre_scores_gemma":[0.8998306,0.00001309688,0.0977671,0.001546301,0.0001956602,0.0001246265,0.0001756621,0.00001137932,0.0003355448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7229243,"threshold_uncertainty_score":0.4084093,"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."}}