{"id":"W1598592993","doi":"10.1109/icassp.2005.1415936","title":"A Recursive Estimation of the Condition Number in the RLS Algorithm","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Recursive least squares filter; Algorithm; Covariance matrix; Covariance; Computer science; Mathematics; Condition number; Adaptive filter; Mathematical optimization; Statistics; Eigenvalues and eigenvectors","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":[0.0000604025,0.00004921351,0.00004850791,0.00001583862,0.00001352401,0.000004775989,0.00009021247,0.00002442776,0.00002483603],"category_scores_gemma":[0.000009961814,0.00002984095,0.00001927959,0.0001142925,0.00002524,0.00007892327,0.0000100533,0.00006570702,0.000005827265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003296358,"about_ca_system_score_gemma":0.000002238521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004530308,"about_ca_topic_score_gemma":0.00001736815,"domain_scores_codex":[0.9997025,0.00001679277,0.000100713,0.00004354822,0.00007320254,0.00006327237],"domain_scores_gemma":[0.9998034,0.00003792587,0.00002020045,0.0001215447,0.00001389281,0.000003077178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000008777456,0.0001553728,0.001420912,0.0001130786,0.00002970802,0.00001006299,0.001507554,0.5772372,0.02176809,0.2648403,0.02923226,0.1036767],"study_design_scores_gemma":[0.0003676223,0.00003290973,0.06349884,0.0001757115,0.00001413697,0.00003090826,0.0002053946,0.314,0.2727769,0.3453333,0.003277337,0.0002868874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05482396,0.00001170599,0.9262506,0.00009011159,0.00005921255,0.0002421912,0.00001175189,0.0001684482,0.01834197],"genre_scores_gemma":[0.9205381,0.000002112618,0.0793125,0.00001962424,0.00001676874,0.00002771442,0.000006314201,0.00000716839,0.00006974847],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8657141,"threshold_uncertainty_score":0.1216879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005437873397930425,"score_gpt":0.2367439685761115,"score_spread":0.2313060951781811,"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."}}