{"id":"W2112662607","doi":"10.1109/icassp.1998.681776","title":"MSE analysis of the M-max NLMS adaptive algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Adaptive filter; Algorithm; Convergence (economics); Least mean squares filter; Stability (learning theory); Adaptive algorithm; Computer science; Mean squared error; Filter (signal processing); Mathematics; Reduction (mathematics); Algorithm design; Mathematical optimization; Statistics; Machine learning","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":[0.00003610086,0.0001028626,0.0001787311,0.0001175539,0.00002211068,0.000004649365,0.0001958197,0.00003873725,0.0004567867],"category_scores_gemma":[0.00001079687,0.00007309824,0.0001353484,0.0007216424,0.00004527058,0.00007472469,0.00005308382,0.00009219847,0.00001163027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004221398,"about_ca_system_score_gemma":0.000001046989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001503298,"about_ca_topic_score_gemma":0.00001327794,"domain_scores_codex":[0.9994871,0.00001175017,0.0001473572,0.000103639,0.0001184776,0.0001316629],"domain_scores_gemma":[0.9995517,0.00003314663,0.0000268204,0.0003297516,0.00003309043,0.00002549893],"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":[0.000008596275,0.0001855474,0.001632333,0.00003649572,0.004941314,0.00001614572,0.001775764,0.5582279,0.04234476,0.01698881,0.0123265,0.3615158],"study_design_scores_gemma":[0.00005530395,0.00002110231,0.002871317,0.00001032757,0.0001579039,8.62125e-7,0.000039722,0.9532951,0.04052546,0.0004091046,0.002474698,0.0001391485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007240949,0.0001514052,0.9673614,0.00002323738,0.00007848829,0.0001180037,0.00003396716,0.0006149433,0.0243776],"genre_scores_gemma":[0.8847051,0.00003747338,0.1141687,0.00002185791,0.00001686416,0.00001223959,0.000001092925,0.00001863331,0.001018094],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8774641,"threshold_uncertainty_score":0.5001494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799765018733172,"score_gpt":0.2039263970693402,"score_spread":0.1859287468820085,"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."}}