{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001290387,0.0007197812,0.001135557,0.0009770404,0.0005534867,0.0008353852,0.001145339,0.001121819,0.003478003],"category_scores_gemma":[0.004578938,0.0006596259,0.0008084949,0.0009576369,0.0004153796,0.0010431,0.0008963306,0.002249686,0.001911701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000316865,"about_ca_system_score_gemma":0.0006274828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002010328,"about_ca_topic_score_gemma":0.00272989,"domain_scores_codex":[0.9992626,0.0002583585,0.00003759297,0.00007978432,0.0003024636,0.00005906944],"domain_scores_gemma":[0.9986163,0.0006027223,0.00007229762,0.0001743507,0.0004973275,0.00003693791],"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":[0.000246189,0.00008734316,0.0003364497,0.0001543186,0.0000651897,0.00005424051,0.00008093576,0.147742,0.02014077,0.02087202,0.005927879,0.8042925],"study_design_scores_gemma":[0.00001405929,0.00002108059,0.0001192288,0.000009039808,0.00001093522,0.00002773852,0.000006405753,0.988605,0.003328103,0.006272865,0.001578709,0.000006922004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002375876,0.0001778784,0.9964809,0.00004321278,0.00002605278,0.00001157029,0.00001574978,0.0004170049,0.0004516606],"genre_scores_gemma":[0.110848,0.0003729177,0.8847359,0.00005836035,0.00009380977,0.0001183873,0.0001929347,0.0003109524,0.003268821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003478003,"threshold_uncertainty_score":0.01163507,"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."}}