{"id":"W2147795503","doi":"10.1109/pacrim.2009.5291404","title":"Fast Group Sparse Classification","year":2009,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Classifier (UML); Optimization problem; Quadratic programming; Greedy algorithm; Computer science; Sparse approximation; Minification; Convex optimization; Mathematical optimization; Linear programming; Linear programming relaxation; Artificial intelligence; Regular polygon; Mathematics; Algorithm","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":[0.00001932628,0.00005836395,0.00005154189,0.00003425023,0.00001687001,0.00001800425,0.00005735382,0.00003417071,0.00003564075],"category_scores_gemma":[0.000001927712,0.00005432891,0.00001949183,0.00006164677,0.00000627842,0.00005812551,0.000003371362,0.00004902564,0.00005712123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001344698,"about_ca_system_score_gemma":0.000001056827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002516057,"about_ca_topic_score_gemma":0.000002131605,"domain_scores_codex":[0.9997212,0.000003852649,0.0000687393,0.00006695349,0.0000491024,0.0000901915],"domain_scores_gemma":[0.9998038,0.000004693743,0.000006477944,0.0001497603,0.00001113518,0.00002417703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004834123,0.00004621811,0.0001562792,0.000003170437,0.00001324971,0.000008378503,0.00008148285,0.001365176,0.5304958,0.0554074,0.07870076,0.3337173],"study_design_scores_gemma":[0.0003753254,0.0001706771,0.1046782,0.00006656117,0.0000233541,0.00003002908,0.0001206575,0.5084254,0.2839628,0.02092246,0.08047573,0.0007488211],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1603273,0.0001648489,0.3337933,0.0003386715,0.000224219,0.0001517125,8.974549e-7,0.005305335,0.4996937],"genre_scores_gemma":[0.9929191,0.00002963163,0.006673284,0.000155243,0.00005336558,0.000001537932,0.000003646822,0.000007004631,0.0001571349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8325918,"threshold_uncertainty_score":0.2215469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02259576042387802,"score_gpt":0.2293559685876077,"score_spread":0.2067602081637297,"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."}}