{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002450248,0.0005059639,0.001033668,0.000716983,0.000443236,0.001321569,0.002324788,0.0008454972,0.003091925],"category_scores_gemma":[0.01176256,0.0002823914,0.0005301185,0.0006919127,0.0007386151,0.002273941,0.001893472,0.001242519,0.0003771161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005222026,"about_ca_system_score_gemma":0.001369982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002597813,"about_ca_topic_score_gemma":0.002245757,"domain_scores_codex":[0.9990047,0.0001990727,0.00009236673,0.0002102326,0.0003915766,0.0001020269],"domain_scores_gemma":[0.9954574,0.001773502,0.000182074,0.000736773,0.001622747,0.0002274981],"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":[0.001912278,0.0003936193,0.004838625,0.0003980522,0.0001556785,0.0003492316,0.000280066,0.2306296,0.01411168,0.04345205,0.003312583,0.7001666],"study_design_scores_gemma":[0.00001141908,0.00009185007,0.0004135827,0.000007935408,0.00001912394,0.00005253878,0.00001951262,0.9916704,0.002318472,0.004885496,0.0005012012,0.000008442591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1649825,0.0005815461,0.8293844,0.0002259921,0.0002241754,0.00007918827,0.0001571302,0.0006789225,0.003686166],"genre_scores_gemma":[0.8176911,0.0001498226,0.1781031,0.00005280265,0.00004389734,0.00005529482,0.000288857,0.00005897976,0.003556163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003091925,"threshold_uncertainty_score":0.01295829,"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."}}