{"id":"W2156642959","doi":"10.1109/cjece.2009.5599420","title":"Fast group sparse classification","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Classifier (UML); Sparse approximation; Optimization problem; Random projection; Minification; Robustness (evolution); Pattern recognition (psychology); Linear programming; Artificial intelligence; Algorithm; Mathematical optimization; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00004634171,0.00009917645,0.0001403983,0.0002373116,0.00002898905,0.0000559975,0.00009851192,0.0000522646,0.000002659388],"category_scores_gemma":[0.000007689488,0.00009641299,0.00004009837,0.0001653016,0.000008110822,0.00009054114,0.000002346371,0.0002097556,9.50865e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005568499,"about_ca_system_score_gemma":0.00002603828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002014323,"about_ca_topic_score_gemma":0.00002041811,"domain_scores_codex":[0.9994887,0.0000055543,0.0001754465,0.00006292025,0.00005988509,0.0002074745],"domain_scores_gemma":[0.9995658,0.00002140182,0.00002394039,0.00006603339,0.00003895013,0.000283818],"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":[0.000009806054,0.00002533429,0.0008505636,0.000018331,0.0001051578,0.0004307076,0.0002453943,0.09563652,0.0257287,0.01735817,0.01235582,0.8472355],"study_design_scores_gemma":[0.0001666474,0.0002353507,0.0352462,0.00007524495,0.00001520696,0.0004092851,0.000002087841,0.9520628,0.001296492,0.0005044218,0.00977872,0.0002075524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2566251,0.002249317,0.7400082,0.0001492999,0.0003730565,0.00005624424,8.272817e-7,0.0001385943,0.00039934],"genre_scores_gemma":[0.9932908,0.00005667489,0.006272252,0.00008261689,0.000283673,2.935978e-7,6.570555e-7,0.00001003505,0.000003041384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8564263,"threshold_uncertainty_score":0.3931607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008920718750992356,"score_gpt":0.173665880950318,"score_spread":0.1647451621993256,"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."}}