{"id":"W2605427908","doi":"10.1016/j.neunet.2017.04.001","title":"Recursive least mean<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si85.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>p</mml:mi></mml:math>-power Extreme Learning Machine","year":2017,"lang":"en","type":"article","venue":"Neural Networks","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; Nunavut General Monitoring Plan","keywords":"Extreme learning machine; Algorithm; Computer science; Line (geometry); Mean squared error; Generalization; Artificial intelligence; Power (physics); Function (biology); Machine learning; Artificial neural network; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007592216,0.0004798744,0.000321594,0.00009678836,0.001748919,0.001113252,0.001931425,0.000464136,0.00006742145],"category_scores_gemma":[0.0004375564,0.0005171522,0.0004399327,0.0002371565,0.0002728343,0.0009593299,0.001359886,0.001528932,0.0005336216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000189765,"about_ca_system_score_gemma":0.00009956577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004484643,"about_ca_topic_score_gemma":0.0001891226,"domain_scores_codex":[0.9964166,0.0002069382,0.0005541947,0.0009984563,0.0007639201,0.001059871],"domain_scores_gemma":[0.9967744,0.0003440687,0.0007810149,0.001651078,0.00008606751,0.0003633957],"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":[0.0001649939,0.00009480246,0.0003395182,0.00004979057,0.0001080565,0.0003693259,0.0008367561,0.06041773,0.00009484099,0.909913,0.001825289,0.02578588],"study_design_scores_gemma":[0.0006769599,0.0004484107,0.000980972,0.0001937858,0.00006376806,0.0002431188,0.00005375476,0.9873829,0.00009602943,0.0002152025,0.00917042,0.0004746942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9710969,0.0006315483,0.01812658,0.001563917,0.002527314,0.0000753908,0.000009566736,0.0004035782,0.005565145],"genre_scores_gemma":[0.9943676,0.0001452909,0.002334141,0.0009803141,0.001150698,0.00005017951,0.00006707064,0.0001122513,0.0007924803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9269652,"threshold_uncertainty_score":0.9999237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01835045415857543,"score_gpt":0.2454426133669017,"score_spread":0.2270921592083263,"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."}}