{"id":"W2134065443","doi":"10.1109/icassp.2002.5745814","title":"A variable weight mixed-norm adaptive algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Norm (philosophy); Convergence (economics); Algorithm; Computer science; Mathematics; Variable (mathematics); Adaptive filter; Applied mathematics; Mathematical optimization","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":[0.000560523,0.0005543386,0.0006740029,0.0003672867,0.0003016956,0.0009347649,0.001130433,0.001117694,0.003764709],"category_scores_gemma":[0.001429577,0.0002305498,0.0003589028,0.0004894622,0.0004405213,0.0008245595,0.0007702873,0.0008213377,0.001752745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002717097,"about_ca_system_score_gemma":0.0006626946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000722645,"about_ca_topic_score_gemma":0.0006290513,"domain_scores_codex":[0.9994962,0.000112995,0.00002336849,0.0001173919,0.0002211804,0.00002888062],"domain_scores_gemma":[0.9996858,0.00007854948,0.00002363195,0.00003504334,0.0001561698,0.00002095798],"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":[0.0002259394,0.00008459843,0.0004292071,0.0002659083,0.00008852372,0.0001597884,0.00008530862,0.3068917,0.04645776,0.1188376,0.00578946,0.5206842],"study_design_scores_gemma":[0.00001548726,0.00004091416,0.00006493479,0.000008530419,0.000007480741,0.0000640646,0.000003720063,0.9860696,0.003459635,0.005557977,0.004698511,0.000009234628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001649815,0.000123552,0.9957958,0.0000609621,0.00006079414,0.00001751835,0.00001309836,0.0001749779,0.002103521],"genre_scores_gemma":[0.1950656,0.0004641794,0.7876988,0.000198032,0.0001547543,0.0002308156,0.0001737658,0.0001154634,0.01589859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003764709,"threshold_uncertainty_score":0.01259422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409280037548909,"score_gpt":0.1900048110849622,"score_spread":0.1759120107094731,"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."}}