{"id":"W3012677073","doi":"10.1016/j.ins.2020.03.038","title":"Incremental and decremental fuzzy bounded twin support vector machine","year":2020,"lang":"en","type":"article","venue":"Information Sciences","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Hyperplane; Support vector machine; Computer science; Bounded function; Binary classification; Directed acyclic graph; Artificial intelligence; Fuzzy logic; Hinge loss; Algorithm; Mathematics; Pattern recognition (psychology)","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":[0.0004759168,0.00008537837,0.00008233872,0.00008224745,0.0003369719,0.0006335958,0.0005050577,0.0000207581,0.00007909008],"category_scores_gemma":[0.00007875504,0.00006981598,0.00002138421,0.0004065491,0.0001130446,0.003024373,0.0002491894,0.00006847909,0.0002107019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001578523,"about_ca_system_score_gemma":0.00007311998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001151869,"about_ca_topic_score_gemma":0.000007838153,"domain_scores_codex":[0.9989724,0.00002903582,0.0002297426,0.000148976,0.0004397366,0.0001801227],"domain_scores_gemma":[0.9996181,0.00002696677,0.0001010137,0.00009216873,0.00002557735,0.0001362219],"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":[0.00005088443,0.00009093332,0.06507451,0.000131833,0.00004278206,0.00001073983,0.05318196,0.0006005737,0.002474405,0.1939504,0.0223036,0.6620874],"study_design_scores_gemma":[0.001683834,0.001192661,0.05594008,0.00001939201,0.000008531127,0.00009062724,0.001492357,0.72877,0.003505698,0.001868883,0.2047828,0.0006451964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7118738,0.00009307516,0.1572116,0.03290537,0.0006531104,0.0003966691,0.00001734286,0.0006567236,0.09619233],"genre_scores_gemma":[0.9853173,0.000004395903,0.009678113,0.004941902,0.00002984979,0.000003305138,0.000006481665,0.00000124488,0.00001735768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7281694,"threshold_uncertainty_score":0.6109779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02232584499365439,"score_gpt":0.2647570376424209,"score_spread":0.2424311926487665,"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."}}