{"id":"W2731824094","doi":"","title":"New fuzzy multi-class methode to train SVM classifier","year":2011,"lang":"en","type":"article","venue":"Databases, Knowledge, and Data Applications","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Fuzzy logic; Pattern recognition (psychology); Classifier (UML); Class (philosophy); Machine learning; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001870994,0.0002497608,0.0002247982,0.0001199915,0.0002049054,0.00003499206,0.0007335236,0.00006313285,0.0001219],"category_scores_gemma":[0.00003458349,0.0002557222,0.00002860121,0.000504617,0.00005942328,0.0004199589,0.0003916244,0.0001975171,0.0005323071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003082083,"about_ca_system_score_gemma":0.00005643993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001346261,"about_ca_topic_score_gemma":0.0008533273,"domain_scores_codex":[0.9985852,0.00002063571,0.0003196545,0.00065277,0.00008755799,0.0003341479],"domain_scores_gemma":[0.9973295,0.000110614,0.00003646382,0.00202058,0.00005327278,0.0004495497],"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":[0.000006641698,0.0003221549,0.00007311481,0.00008789211,0.00007739992,0.000001388108,0.0005891865,0.00007090147,0.003644155,0.09503456,0.1797764,0.7203162],"study_design_scores_gemma":[0.0002954503,0.000007955874,0.0007501998,0.00001497945,0.00005406296,0.000006754433,0.0001241682,0.0134263,0.0007905698,0.0010872,0.9830989,0.0003434416],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000415963,0.001701513,0.9766606,0.00008375677,0.00008544399,0.0007153068,0.008540206,0.0003873395,0.01178423],"genre_scores_gemma":[0.003059851,0.0007229215,0.9854563,0.0001434096,0.0004240443,0.0009292007,0.007763946,0.00008253004,0.001417733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8033226,"threshold_uncertainty_score":0.9999895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1444481156309096,"score_gpt":0.3555506836801995,"score_spread":0.21110256804929,"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."}}