{"id":"W1526572406","doi":"10.1023/a:1021938425987","title":"Fuzzy Inductive Learning Strategies","year":2003,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"National Taiwan University; National Chiao Tung University","keywords":"Computer science; Artificial intelligence; Machine learning; Multi-task learning; Set (abstract data type); Fuzzy logic; Task (project management)","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":[0.001356122,0.0007000486,0.0005389358,0.001318959,0.0009506263,0.001727899,0.001738281,0.0009616416,0.01125951],"category_scores_gemma":[0.004114938,0.0002678046,0.0006540321,0.0008415387,0.001373486,0.002260623,0.002017342,0.001455816,0.00336744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008972043,"about_ca_system_score_gemma":0.0007174268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005943598,"about_ca_topic_score_gemma":0.0008392132,"domain_scores_codex":[0.9993318,0.0001964471,0.00003124794,0.0001244938,0.0002567986,0.00005923193],"domain_scores_gemma":[0.9990945,0.0004567498,0.00003948927,0.0001590703,0.0002097174,0.00004040642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001915335,0.00004139425,0.0001378205,0.00008960151,0.00002548949,0.00003826714,0.000144335,0.007868543,0.001111071,0.8301642,0.003964425,0.1563957],"study_design_scores_gemma":[0.00001512304,0.00003161455,0.0000957519,0.00005259415,0.00002629363,0.0001029294,0.0000658031,0.07350372,0.00375968,0.8899392,0.0323921,0.00001511338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005144238,0.001071592,0.9185012,0.000796738,0.000182508,0.00007982869,0.00004670228,0.0002894491,0.07388773],"genre_scores_gemma":[0.4385561,0.003518746,0.4265474,0.00109742,0.0006375989,0.0004680816,0.0003101001,0.0002147873,0.1286498],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01125951,"threshold_uncertainty_score":0.03766686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01732436954886669,"score_gpt":0.2314054289785562,"score_spread":0.2140810594296895,"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."}}