{"id":"W2048291033","doi":"10.1016/j.patrec.2010.06.014","title":"Improved Group Sparse Classifier","year":2010,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Classifier (UML); Artificial intelligence; Margin classifier; Computer science; Quadratic classifier; Pattern recognition (psychology); Regular polygon; Machine learning; Optimization problem; 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":[0.00006068428,0.0001568034,0.0001163869,0.0000948125,0.00004376812,0.00005594127,0.0001098091,0.00009069504,0.000300776],"category_scores_gemma":[0.000008329341,0.0001640083,0.00006702522,0.00006935195,0.00003926481,0.0001334593,0.00001955049,0.0003609767,0.0002229833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000146541,"about_ca_system_score_gemma":0.000001964107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002578443,"about_ca_topic_score_gemma":0.00005048422,"domain_scores_codex":[0.9993209,0.00001577213,0.0001599309,0.0001775003,0.00009003655,0.0002358623],"domain_scores_gemma":[0.9996223,0.00002882133,0.00003001279,0.000226668,0.00002549745,0.00006664183],"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":[0.000002602281,0.0000120341,0.0004329189,0.000008316757,0.00001977655,0.00001314846,0.00003331552,0.000007336565,0.8111281,0.000001595815,0.008953908,0.1793869],"study_design_scores_gemma":[0.001388113,0.00008248318,0.01430907,0.0001824704,0.00009983609,0.0001488275,0.0000524788,0.07981256,0.8610121,0.0009836935,0.04021141,0.00171697],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9148666,0.000008544062,0.08042084,0.000436595,0.001222491,0.0001538596,0.00001669741,0.001149875,0.001724531],"genre_scores_gemma":[0.993696,0.00001125212,0.002976446,0.00277514,0.000387261,0.00003746922,0.00006037962,0.00004737872,0.00000870654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1776699,"threshold_uncertainty_score":0.6688063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01852208099439242,"score_gpt":0.2099244428608923,"score_spread":0.1914023618664999,"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."}}