{"id":"W3128464108","doi":"10.1109/tcsii.2021.3056729","title":"Kernel Recursive Maximum Versoria Criterion Algorithm Using Random Fourier Features","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Ministry of Electronics and Information technology","keywords":"Reproducing kernel Hilbert space; Algorithm; Computer science; Kernel (algebra); Convergence (economics); Feature (linguistics); Radar; Fourier transform; Mathematical optimization; Mathematics; Hilbert space; Telecommunications","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":[0.000794292,0.0005671943,0.0007951591,0.0005880329,0.0003124469,0.0008823103,0.0008312485,0.0009108394,0.001725782],"category_scores_gemma":[0.002501127,0.0002442475,0.0005313111,0.0004808932,0.0005790562,0.0008904146,0.0007709287,0.000769668,0.0005495538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005597086,"about_ca_system_score_gemma":0.001409075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002279993,"about_ca_topic_score_gemma":0.002261735,"domain_scores_codex":[0.9996067,0.0001120358,0.00002629886,0.00007382525,0.0001354743,0.00004566339],"domain_scores_gemma":[0.9994438,0.0002313364,0.000070032,0.00006209795,0.0001667294,0.00002596252],"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":[0.0002387289,0.00006935646,0.000881919,0.0001822599,0.00005978764,0.000138805,0.0001313584,0.553189,0.02139996,0.07189966,0.004468385,0.3473408],"study_design_scores_gemma":[0.000005793941,0.00002424559,0.00008738013,0.000005282895,0.000003350571,0.00003064091,0.00000421486,0.9947789,0.001760926,0.002449282,0.0008425867,0.000007441157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004979357,0.0001637103,0.9935464,0.00005959518,0.0000229152,0.00002713874,0.0000195965,0.0002079715,0.0009734349],"genre_scores_gemma":[0.2446227,0.0003332113,0.7499409,0.0001014014,0.00006376718,0.0001715025,0.000185307,0.0001183573,0.004462779],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002279993,"threshold_uncertainty_score":0.005773365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01977114447269356,"score_gpt":0.2453400046317636,"score_spread":0.2255688601590701,"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."}}