{"id":"W2156791867","doi":"10.1023/a:1021726007566","title":"The Minimum Number of Errors in the N-Parity and its Solution with an Incremental Neural Network","year":2002,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial neural network; Computational intelligence; Constructive; Feedforward neural network; Perceptron; Parity (physics); Activation function; Computer science; Multilayer perceptron; Algorithm; Artificial intelligence; Mathematics; Pattern recognition (psychology); Physics","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":[0.001391925,0.0009733522,0.00133366,0.0006514905,0.0006954286,0.001258615,0.001838108,0.004292965,0.004937243],"category_scores_gemma":[0.011879,0.0006831368,0.0006999109,0.0006883565,0.001540991,0.00244989,0.00168888,0.001959987,0.0004195383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006851276,"about_ca_system_score_gemma":0.001257396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003555963,"about_ca_topic_score_gemma":0.003132382,"domain_scores_codex":[0.9995338,0.0001584866,0.00002716795,0.0001268738,0.00008900215,0.00006476588],"domain_scores_gemma":[0.9958628,0.003421046,0.0001866683,0.0001420857,0.0002876723,0.00009976802],"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":[0.000403281,0.00007130663,0.0006238553,0.0003539172,0.00006797446,0.0001943165,0.0001353196,0.8483588,0.00237932,0.06785183,0.004413584,0.07514656],"study_design_scores_gemma":[0.00002599185,0.00003473527,0.0001021971,0.00001792987,0.00001166422,0.00003559957,0.00001880485,0.9593241,0.0007114718,0.03939723,0.0003099436,0.00001037359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05890157,0.0005612463,0.9300717,0.001614524,0.0002791539,0.0000629169,0.0001263105,0.0002541904,0.008128339],"genre_scores_gemma":[0.5566294,0.0003009596,0.4321078,0.0003270166,0.0002363265,0.0002085622,0.0002083835,0.0003269696,0.009654608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004937243,"threshold_uncertainty_score":0.01651669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271416715162606,"score_gpt":0.2593501361350556,"score_spread":0.232208464618795,"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."}}