{"id":"W1958708463","doi":"10.1109/iscas.1992.230013","title":"Convergence and generalization properties of multilayer feedforward neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Backpropagation; Generalization; Artificial neural network; Convergence (economics); Rprop; Computer science; Feed forward; Feedforward neural network; Artificial intelligence; Algorithm; Function (biology); Process (computing); Activation function; Mathematics; Types of artificial neural networks; Time delay neural network; Engineering; Control engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004881112,0.0005657029,0.0006324602,0.001230422,0.0004222649,0.0007732618,0.0005796672,0.0008558627,0.001252832],"category_scores_gemma":[0.02631531,0.0002854308,0.000650213,0.0006642773,0.0007897622,0.001773287,0.0006299975,0.0007381517,0.0002838436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006843529,"about_ca_system_score_gemma":0.0005423467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003331781,"about_ca_topic_score_gemma":0.001442848,"domain_scores_codex":[0.9989771,0.0002671652,0.0000875239,0.0001678059,0.0004137711,0.00008668524],"domain_scores_gemma":[0.9887879,0.007378551,0.000972218,0.0007280396,0.00200633,0.0001269797],"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":[0.0002924236,0.00004282867,0.004357488,0.0002276722,0.00009982735,0.0001550867,0.0002632956,0.8651508,0.01183576,0.01303021,0.0008129346,0.1037318],"study_design_scores_gemma":[0.000007903093,0.00006031484,0.001858843,0.00003098119,0.00002286182,0.0001016337,0.00002131265,0.9879619,0.003511189,0.00605758,0.0003473969,0.00001796897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3232783,0.002731514,0.662484,0.00048052,0.0000831814,0.00009492911,0.0002098478,0.000762562,0.009875158],"genre_scores_gemma":[0.9417471,0.001064752,0.05505081,0.00005748143,0.00004837869,0.00009484734,0.0002115423,0.0001469399,0.001578189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004881112,"threshold_uncertainty_score":0.02581412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084182134114359,"score_gpt":0.2180529085774212,"score_spread":0.1972110872362776,"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."}}