{"id":"W3213216575","doi":"10.1016/j.fss.2021.10.007","title":"Double iterative learning-based polynomial based-RBFNNs driven by the aid of support vector-based kernel fuzzy clustering and least absolute shrinkage deviations","year":2021,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Key Technology Research and Development Program of Shandong; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning","keywords":"Robustness (evolution); Support vector machine; Fuzzy logic; Mathematics; Gaussian function; Cluster analysis; Polynomial; Polynomial regression; Algorithm; Mean squared error; Kernel (algebra); Radial basis function; Gaussian; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Linear regression; Computer science; Statistics","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":[0.001203101,0.0005840331,0.001393388,0.0004639018,0.0005324333,0.001001145,0.00185096,0.00113588,0.001915344],"category_scores_gemma":[0.003196279,0.0003732192,0.0008188643,0.000812189,0.0005246053,0.00127487,0.001061845,0.00124189,0.0009240981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006416576,"about_ca_system_score_gemma":0.001251071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005098222,"about_ca_topic_score_gemma":0.005129345,"domain_scores_codex":[0.9992355,0.0001489771,0.00005131501,0.0001895333,0.0002986416,0.00007597099],"domain_scores_gemma":[0.998924,0.000228475,0.00009658182,0.0001015549,0.0006049493,0.0000445053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003134883,0.0001199587,0.001081578,0.0002317256,0.0001192895,0.00008728306,0.0001720592,0.6132482,0.0183249,0.02161271,0.002824015,0.3418647],"study_design_scores_gemma":[0.000002788266,0.00001501845,0.00006996306,0.000003034874,0.000005603691,0.00001394726,0.000003282045,0.9980125,0.000908325,0.0006927321,0.0002675082,0.000005338392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009620111,0.0002019372,0.9885661,0.00005063543,0.00004702187,0.00001871311,0.00001993828,0.0003038082,0.001171831],"genre_scores_gemma":[0.5662564,0.0003522426,0.4281853,0.00008327199,0.00009581248,0.000116347,0.0002113814,0.0001603343,0.00453895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005098222,"threshold_uncertainty_score":0.01013708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01891421715365089,"score_gpt":0.2475154380309017,"score_spread":0.2286012208772508,"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."}}