{"id":"W7133391277","doi":"","title":"Divide-and-Conquer for Debiased <i>l</i><sub>1</sub>-norm Support Vector Machine in Ultra-high Dimensions","year":2018,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"City University of Hong Kong","keywords":"Support vector machine; Hessian matrix; Estimator; Extension (predicate logic); Convergence (economics); Set (abstract data type); Rate of convergence; Relevance vector machine; Matrix (chemical analysis)","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":[0.0005068799,0.0001883992,0.0002222088,0.0001836462,0.0002715236,0.0002334734,0.0003919998,0.00013026,0.00003580708],"category_scores_gemma":[0.0002625938,0.0001721517,0.00007007669,0.0003162286,0.00008900938,0.001020878,0.0001407874,0.0002593554,0.0001760911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000373039,"about_ca_system_score_gemma":0.00007056691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005124581,"about_ca_topic_score_gemma":0.0001335834,"domain_scores_codex":[0.9984641,0.00007302116,0.0002937243,0.0005282829,0.0002310793,0.0004098002],"domain_scores_gemma":[0.9989417,0.0001950675,0.00008492591,0.0004279577,0.0001535442,0.0001968317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008267168,0.0002358405,0.009444705,0.00004135254,0.00003047978,0.0000285843,0.0006173666,0.000009582461,0.9204798,0.002710326,0.01066777,0.05565147],"study_design_scores_gemma":[0.002354692,0.0003899664,0.0375525,0.0001414823,0.0000231957,0.00003189523,0.00001624027,0.001867253,0.9356724,0.01059961,0.01081184,0.000538901],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612831,0.00006586091,0.03604157,0.001127155,0.0005844853,0.0004089753,0.0000450728,0.000133628,0.000310117],"genre_scores_gemma":[0.9931021,0.0000391523,0.00431814,0.002261029,0.00009011338,0.00007957197,0.00004170269,0.0000175504,0.00005068479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05511257,"threshold_uncertainty_score":0.7020143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589888579282165,"score_gpt":0.2463766793316547,"score_spread":0.2304777935388331,"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."}}