{"id":"W2128491622","doi":"10.1109/tcsii.2010.2048355","title":"A Variable Regularization Method for Affine Projection Algorithm","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Algorithm; Regularization (linguistics); Mathematics; Affine transformation; A priori and a posteriori; Rate of convergence; Computer science; Applied mathematics; Artificial intelligence","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":[0.0009475234,0.0007200564,0.0007902838,0.0005429281,0.0004765281,0.0009620581,0.0009016203,0.001035277,0.003786843],"category_scores_gemma":[0.002083253,0.0003513582,0.0008588928,0.0008286787,0.0008662117,0.001114613,0.001186598,0.002069316,0.00141688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004163276,"about_ca_system_score_gemma":0.001219248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001551564,"about_ca_topic_score_gemma":0.001230311,"domain_scores_codex":[0.9991342,0.0003166623,0.00003585358,0.0001755894,0.0002911982,0.0000464256],"domain_scores_gemma":[0.999449,0.0002139764,0.00004712578,0.00007673475,0.0001848027,0.00002834611],"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":[0.0001303434,0.00006635872,0.0005642396,0.0002213695,0.0001154195,0.0001304153,0.0001387234,0.3677122,0.02608674,0.1822511,0.006583801,0.4159992],"study_design_scores_gemma":[0.000009184149,0.00003216651,0.0000857573,0.000009993078,0.000007571936,0.00007053807,0.000006789026,0.9813231,0.00255436,0.01035746,0.005527396,0.00001568672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004901631,0.00007149189,0.9987864,0.0000315936,0.00002026226,0.000009064036,0.000007593558,0.00007743701,0.0005059807],"genre_scores_gemma":[0.07158637,0.0005511381,0.9209551,0.0001390392,0.0001344257,0.0002093797,0.0001397002,0.0001727667,0.006112169],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003786843,"threshold_uncertainty_score":0.01266819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01371375649829479,"score_gpt":0.2520932754438333,"score_spread":0.2383795189455385,"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."}}