{"id":"W2059932969","doi":"10.1016/s0893-6080(03)00118-7","title":"Automatic basis selection techniques for RBF networks","year":2003,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Basis (linear algebra); Radial basis function; Generalization; Computer science; Estimator; Basis function; Artificial neural network; Variance (accounting); Bayesian information criterion; Artificial intelligence; Radial basis function network; Model selection; Algorithm; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000298298,0.0002550271,0.0002484564,0.00007238207,0.0004304579,0.0002545384,0.0006042534,0.0001767246,0.00002843807],"category_scores_gemma":[0.00002758501,0.0002328035,0.0001662826,0.0008928682,0.00004165228,0.0003946137,0.0000758177,0.0003073904,0.000004899771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004637964,"about_ca_system_score_gemma":0.00002074337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005971017,"about_ca_topic_score_gemma":0.0000118015,"domain_scores_codex":[0.9981882,0.0001084827,0.0003633983,0.0005552619,0.0001635233,0.0006211486],"domain_scores_gemma":[0.9987972,0.0002820675,0.0001576839,0.000505327,0.0001070958,0.0001506496],"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":[0.00000714986,0.0001064782,0.000573323,0.0000166647,0.00002862224,0.000003093123,0.00002357578,0.1926442,0.0001062427,0.1001375,0.04034436,0.6660088],"study_design_scores_gemma":[0.0001596343,0.0001116269,0.0002311509,0.00001518187,0.00001411278,0.00003666605,0.000001744095,0.9771178,0.0004205074,0.002449131,0.01918191,0.0002605722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002771997,0.0002648931,0.9930158,0.0007592233,0.00054926,0.000821629,9.331644e-7,0.0009874321,0.0008287805],"genre_scores_gemma":[0.9332572,0.00006766924,0.06373641,0.001579082,0.0005370732,0.0005401617,0.000008568483,0.00003443232,0.0002393735],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9304852,"threshold_uncertainty_score":0.9493453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01352219974767399,"score_gpt":0.2517854530177928,"score_spread":0.2382632532701188,"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."}}