{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005339747,0.001110658,0.001753323,0.001264153,0.0009159855,0.001464864,0.002098676,0.0016007,0.003154745],"category_scores_gemma":[0.01882681,0.0005948492,0.0007196026,0.001306265,0.002101289,0.002315832,0.002077844,0.002226616,0.001023533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091767,"about_ca_system_score_gemma":0.001505242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002535643,"about_ca_topic_score_gemma":0.003382004,"domain_scores_codex":[0.9980444,0.0006325942,0.0001503195,0.0004878441,0.0005207753,0.000164191],"domain_scores_gemma":[0.993678,0.003552507,0.0006151705,0.00098075,0.0009937162,0.0001798646],"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":[0.0006014979,0.0002448649,0.003617343,0.000257277,0.000135646,0.0002273822,0.0004267657,0.2331003,0.00952219,0.05744369,0.005007103,0.689416],"study_design_scores_gemma":[0.00001671117,0.00005887224,0.0002785213,0.00001182764,0.00001192257,0.00004988052,0.00003099619,0.976905,0.003040481,0.01857116,0.001012917,0.00001167577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01040199,0.0004439737,0.9876222,0.0002324382,0.00003443812,0.0000495201,0.00002134058,0.0005705317,0.000623626],"genre_scores_gemma":[0.2532213,0.000364765,0.7424127,0.0003442169,0.0001840777,0.000318183,0.0001934631,0.0002239507,0.002737296],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005339747,"threshold_uncertainty_score":0.02823961,"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."}}