{"id":"W7133373697","doi":"","title":"Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification","year":2022,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Statistical learning theory; Support vector machine; Rate of convergence; Reproducing kernel Hilbert space; Statistical learning; Kernel (algebra); Convergence (economics); Linear inequality","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001072541,0.0001059161,0.0002297516,0.00004426913,0.0002297392,0.00002307516,0.0001520102,0.00004131721,0.001699165],"category_scores_gemma":[0.006653575,0.0001021822,0.00007512163,0.000107421,0.00008252175,0.00007121869,0.00005197183,0.0001635102,0.000004770079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003719677,"about_ca_system_score_gemma":0.0001482662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004027353,"about_ca_topic_score_gemma":0.000002440043,"domain_scores_codex":[0.9987607,0.0001342704,0.0004125121,0.0002436689,0.0002590877,0.0001897469],"domain_scores_gemma":[0.996209,0.003080581,0.0001686327,0.0001801875,0.0002849222,0.00007664625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003042386,0.0001842032,0.004566777,0.0001852761,0.00003527244,6.342912e-7,0.00005856373,0.00001399127,0.01113271,0.9725001,0.00625132,0.00476685],"study_design_scores_gemma":[0.001307581,0.001005748,0.06049398,0.00001400799,0.0001428273,0.000006631553,0.0001340729,0.08029146,0.006014308,0.8357295,0.01454886,0.0003109973],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05655525,0.00001199745,0.9398541,0.0002254871,0.0004742543,0.0005596647,0.002164095,0.00002847601,0.0001266037],"genre_scores_gemma":[0.4977342,0.000001723762,0.5010503,0.0001201084,0.00009350564,0.0005447009,0.0001878584,0.00002120695,0.0002464292],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4411789,"threshold_uncertainty_score":0.9992134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.230558225503552,"score_gpt":0.4320637594767343,"score_spread":0.2015055339731822,"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."}}