{"id":"W2102460275","doi":"10.1109/icassp.2009.4959720","title":"Classification via group sparsity promoting regularization","year":2009,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Regularization (linguistics); Training set; Artificial intelligence; Elastic net regularization; Test set; Computer science; Pattern recognition (psychology); Mathematics; Class (philosophy); Sample (material); Benchmark (surveying); Regularization perspectives on support vector machines; Tikhonov regularization; Machine learning; Feature selection; Inverse problem","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":[0.001944557,0.0006613866,0.001355471,0.001132786,0.000418665,0.001010697,0.001177661,0.001386636,0.00136545],"category_scores_gemma":[0.005056368,0.0003383153,0.0008091592,0.0012265,0.001213541,0.001640837,0.001473963,0.001659406,0.0007616828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004942886,"about_ca_system_score_gemma":0.000501094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008148856,"about_ca_topic_score_gemma":0.0007076926,"domain_scores_codex":[0.9985048,0.000544695,0.00005284989,0.0003186481,0.0004799578,0.00009910535],"domain_scores_gemma":[0.9978361,0.001057113,0.0002814451,0.0004302039,0.0003153202,0.00007988344],"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":[0.0005185842,0.0001906324,0.002296838,0.0002431825,0.0001611068,0.0001794995,0.0002707062,0.32138,0.0139175,0.07268883,0.01749185,0.5706612],"study_design_scores_gemma":[0.0000158622,0.00004303728,0.0002249075,0.000007658091,0.00001129336,0.00004485022,0.00001305534,0.9756575,0.001409751,0.02111248,0.001450063,0.000009508079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01064656,0.0001499211,0.9876236,0.0003020352,0.00003724819,0.00002680063,0.00005126289,0.0002252429,0.0009373205],"genre_scores_gemma":[0.4220868,0.0007040784,0.5676045,0.0006804474,0.0006685552,0.0003258668,0.0009721843,0.0001504714,0.006807106],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001944557,"threshold_uncertainty_score":0.01028395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615092189291722,"score_gpt":0.2151156575446244,"score_spread":0.1989647356517072,"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."}}