{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005479094,0.001078881,0.001129664,0.001079871,0.0003416199,0.001300188,0.001040243,0.0008834153,0.0136269],"category_scores_gemma":[0.002938372,0.0002679788,0.0005772133,0.001313819,0.0002660714,0.001287298,0.0007189281,0.001139198,0.01508542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002344978,"about_ca_system_score_gemma":0.0007173342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00149685,"about_ca_topic_score_gemma":0.00120073,"domain_scores_codex":[0.9991642,0.0001261656,0.00006738102,0.0002311299,0.0003463918,0.00006470676],"domain_scores_gemma":[0.999117,0.00021188,0.00006884842,0.0001758847,0.0003881851,0.00003826276],"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":[0.00009562782,0.0001062404,0.0005609965,0.0001525851,0.00004968416,0.00005602925,0.00001208818,0.01718335,0.004181094,0.004367705,0.02600015,0.9472345],"study_design_scores_gemma":[0.00004248944,0.0002597535,0.002186877,0.0001178794,0.00006017157,0.0003435764,0.00005248978,0.8695667,0.01768175,0.02256904,0.08705153,0.00006771642],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01296514,0.004623747,0.9442468,0.0007914634,0.001071236,0.0002853393,0.002936031,0.01107914,0.02200124],"genre_scores_gemma":[0.3441229,0.005290071,0.5632831,0.0007531248,0.0009136532,0.000584776,0.01600655,0.0005958298,0.06844997],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.0136269,"threshold_uncertainty_score":0.04558653,"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."}}