{"id":"W1558917119","doi":"10.1109/icnn.1995.487332","title":"Voting schemes for cooperative neural network classifiers","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Voting; Computer science; Ballot; Modular design; Representation (politics); Artificial neural network; Artificial intelligence; Majority rule; Machine learning; Power (physics); Weighted voting; Modular neural network; Data mining; Time delay neural network","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":[0.00005783271,0.00008005503,0.00008159834,0.00001214563,0.0002739072,0.0001354107,0.0003523642,0.00002655906,0.00004188365],"category_scores_gemma":[0.000009402671,0.00006377014,0.000046351,0.0002746267,0.00002506859,0.0002256654,0.00007735221,0.00006872563,0.0000285926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008040652,"about_ca_system_score_gemma":0.000004015176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001356363,"about_ca_topic_score_gemma":0.00000324196,"domain_scores_codex":[0.9993038,0.00001308854,0.0001207407,0.0002371684,0.00006666873,0.0002585896],"domain_scores_gemma":[0.9995412,0.0001215824,0.00003561974,0.000189029,0.00005027136,0.00006229818],"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":[8.998456e-7,0.00002209771,0.0001348917,0.000002252142,0.000007923644,7.148263e-7,0.00005211398,0.01139065,0.0001005523,0.7695263,0.1382632,0.08049844],"study_design_scores_gemma":[0.0001078596,0.00002827336,0.0000448434,0.00000285489,0.000001580092,0.000002265854,0.000006148499,0.9194752,0.000156238,0.0005602146,0.07952601,0.00008852102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002103736,0.0001285144,0.9862534,0.00582986,0.0001912598,0.0002554773,7.189416e-7,0.0001917338,0.00504527],"genre_scores_gemma":[0.9064996,0.000008016352,0.08764729,0.002031471,0.000331314,0.00007932103,0.000001359938,0.000006606864,0.003395073],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9080845,"threshold_uncertainty_score":0.2600471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04728089764715796,"score_gpt":0.2610657803962663,"score_spread":0.2137848827491083,"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."}}