{"id":"W2128276860","doi":"10.1002/cpe.3492","title":"Sparse support vector machine for pattern recognition","year":2015,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Pattern recognition (psychology); Outlier; Artificial intelligence; Computer science; Sparse approximation; Norm (philosophy); Ranking SVM; Structured support vector machine; Representation (politics); Machine learning","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":[0.001125,0.000873448,0.001162116,0.001626079,0.0002715411,0.001548358,0.001067238,0.001213935,0.007601746],"category_scores_gemma":[0.00626796,0.000266887,0.0007377389,0.002756275,0.000739272,0.001305196,0.0009858439,0.001865042,0.004774749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005364903,"about_ca_system_score_gemma":0.0006898939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001417712,"about_ca_topic_score_gemma":0.0008760109,"domain_scores_codex":[0.9985441,0.0004351377,0.0001260493,0.0003208462,0.000505933,0.00006802714],"domain_scores_gemma":[0.9979472,0.0009785577,0.0002328098,0.0003005222,0.0004869483,0.00005406487],"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":[0.0000962265,0.00005718968,0.0009339513,0.0006907086,0.0001196021,0.0001813838,0.00007957709,0.07356717,0.00654554,0.05956397,0.03094294,0.8272217],"study_design_scores_gemma":[0.00001551478,0.00007576016,0.0007806008,0.000148796,0.00003081889,0.0002090268,0.00004905931,0.837152,0.003066973,0.1100338,0.04839834,0.00003945633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003126186,0.008980952,0.9794796,0.001443931,0.0003778292,0.00007570656,0.0004930331,0.001747007,0.004275729],"genre_scores_gemma":[0.260982,0.01584917,0.7023733,0.000841391,0.001884328,0.0004502271,0.00325713,0.0002929412,0.01406945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007601746,"threshold_uncertainty_score":0.02543032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07136342235487081,"score_gpt":0.3262316526870569,"score_spread":0.2548682303321861,"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."}}