{"id":"W3148199450","doi":"10.1007/978-0-387-73003-5_299","title":"Support Vector Machine","year":2009,"lang":"en","type":"article","venue":"Encyclopedia of Biometrics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Hyperplane; Support vector machine; Structural risk minimization; Margin classifier; Artificial intelligence; Structured support vector machine; Maximization; Classifier (UML); Machine learning; Pattern recognition (psychology); Computer science; Margin (machine learning); Generalization; Minification; Relevance vector machine; Linear classifier; Mathematics; Mathematical optimization; Combinatorics","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":[0.0001885668,0.00009183172,0.0001392125,0.0008591866,0.00003710822,0.00002711075,0.0004883789,0.0000632774,0.0000952427],"category_scores_gemma":[0.0001560109,0.00007915057,0.00006064901,0.003212171,0.00001621216,0.0003004514,0.00006903273,0.00007525384,0.0001450094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001356887,"about_ca_system_score_gemma":0.00003915425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007643789,"about_ca_topic_score_gemma":3.650499e-7,"domain_scores_codex":[0.9990379,0.00002013108,0.0002286975,0.0002066085,0.000322715,0.0001839262],"domain_scores_gemma":[0.9993374,0.00007522593,0.00009976634,0.0003157798,0.00007916732,0.0000926452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006777225,0.0002869662,0.0013594,0.00001574619,0.00000767196,0.00002033817,0.0002660864,0.000003179756,0.003590436,0.003110312,0.036289,0.9550441],"study_design_scores_gemma":[0.001679629,0.002186403,0.1057987,0.00005906954,0.00002970184,0.00004030338,0.00003783642,0.005259934,0.05866329,0.01144349,0.8138415,0.0009601725],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.092471,0.001709057,0.5664622,0.004951063,0.003196026,0.0004894792,0.0000651673,0.0006072855,0.3300487],"genre_scores_gemma":[0.9246419,0.001348298,0.0710972,0.000718957,0.000122979,0.000004014903,0.00002856343,0.000007790951,0.002030295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9540839,"threshold_uncertainty_score":0.3227666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01119250399503802,"score_gpt":0.2459390715849845,"score_spread":0.2347465675899465,"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."}}