{"id":"W2074743521","doi":"10.1016/j.ins.2007.06.022","title":"Fuzzy functions with support vector machines","year":2007,"lang":"en","type":"article","venue":"Information Sciences","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Support vector machine; Fuzzy classification; Fuzzy logic; Fuzzy set operations; Data mining; Defuzzification; Neuro-fuzzy; Fuzzy rule; Artificial intelligence; Computer science; Fuzzy number; Fuzzy clustering; Fuzzy set; Mathematics; Fuzzy associative matrix; Machine learning; Pattern recognition (psychology); Fuzzy control system","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":[0.001065524,0.00007871565,0.00008185357,0.00017754,0.0003453625,0.0003864055,0.0005768877,0.00002749798,0.00001160226],"category_scores_gemma":[0.00003075117,0.00004914474,0.00002580711,0.0007959967,0.000118332,0.003779419,0.00005162896,0.0000519207,0.0004476266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002015866,"about_ca_system_score_gemma":0.00011734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008301227,"about_ca_topic_score_gemma":0.0000524519,"domain_scores_codex":[0.9988765,0.00001284399,0.0002586226,0.0001177577,0.0004939765,0.0002403667],"domain_scores_gemma":[0.9994264,0.00006805736,0.0001295714,0.0001866933,0.0001120659,0.0000771789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002504545,0.00003813641,0.01542917,0.00001991803,0.00001731499,0.000006998108,0.004028876,0.001152949,0.0001256834,0.708948,0.007844442,0.2623635],"study_design_scores_gemma":[0.00357673,0.003641218,0.4145271,0.00008932305,0.00002909174,0.0009335846,0.006876575,0.05950953,0.000801286,0.03596578,0.472049,0.002000873],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.009369597,0.00001525057,0.4701961,0.0009708481,0.0005781386,0.0001282097,0.00000197027,0.0001891117,0.5185508],"genre_scores_gemma":[0.99429,7.685898e-7,0.004506106,0.0007874487,0.00005750434,0.00000810953,0.000002201191,9.330909e-7,0.0003469106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9849204,"threshold_uncertainty_score":0.5753483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323109757490318,"score_gpt":0.2363759735314293,"score_spread":0.2231448759565261,"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."}}