{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009223177,0.0007634716,0.000784998,0.0006961388,0.0005822437,0.001744812,0.002215149,0.001081911,0.09367272],"category_scores_gemma":[0.008075704,0.0004260543,0.0007180971,0.001156672,0.0004394113,0.001968273,0.001342161,0.001761649,0.05523399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007772179,"about_ca_system_score_gemma":0.001310742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003482066,"about_ca_topic_score_gemma":0.008261332,"domain_scores_codex":[0.999321,0.0001359762,0.00003950271,0.0001561387,0.0002897182,0.0000576488],"domain_scores_gemma":[0.9979547,0.0006596489,0.0001039866,0.0005950158,0.0006384798,0.00004814769],"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":[0.0001400802,0.00009086872,0.0009416787,0.0003490211,0.00008201902,0.0001140391,0.0001307176,0.0515868,0.006921712,0.1197163,0.3008403,0.5190864],"study_design_scores_gemma":[0.00006864909,0.00006327762,0.001427148,0.00009461087,0.00004165879,0.0002351567,0.0000558259,0.6188241,0.02631834,0.1230873,0.2296977,0.0000862582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002261505,0.0001628986,0.9573914,0.000556835,0.000190873,0.00004780394,0.001908988,0.01546814,0.02201166],"genre_scores_gemma":[0.06897438,0.0002843244,0.8326765,0.0005829781,0.0002314351,0.0002484346,0.007350811,0.01252369,0.07712743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09367272,"threshold_uncertainty_score":0.3133665,"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."}}