{"id":"W2080712811","doi":"10.1109/hpcsim.2013.6641476","title":"Sparse Support Vector Machine for pattern recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Pattern recognition (psychology); Artificial intelligence; Computer science; Generalization; Outlier; Sparse approximation; Structured support vector machine; Representation (politics); Machine learning; Relevance vector machine; Ranking SVM; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008597667,0.00008188874,0.00007958478,0.00005130635,0.00006231885,0.0001208299,0.0002135756,0.0000435369,0.002266928],"category_scores_gemma":[0.00001771078,0.00006449327,0.00005187754,0.00007198496,0.000008103974,0.0006311164,0.00005932689,0.00004415596,0.003504208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008943646,"about_ca_system_score_gemma":0.00001369908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001201934,"about_ca_topic_score_gemma":0.00001561466,"domain_scores_codex":[0.9993313,0.00001635739,0.0001393574,0.0002230138,0.0001042655,0.0001857347],"domain_scores_gemma":[0.9995307,0.00004989752,0.00004053069,0.0001989245,0.0001023572,0.00007756501],"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":[0.000002466876,0.00006020808,0.0002389474,0.00001534193,0.000006167969,0.000001236431,0.00008350756,7.530467e-7,0.00378846,0.0001080669,0.1046422,0.8910526],"study_design_scores_gemma":[0.005165509,0.001603424,0.01914312,0.0001800827,0.00004257421,0.00009079898,0.0001597151,0.4937969,0.2934,0.09475178,0.0896633,0.00200288],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02531777,0.000007546459,0.9638614,0.003180286,0.0004474889,0.0004867209,0.00001707414,0.0002422583,0.006439477],"genre_scores_gemma":[0.9122254,0.00001286735,0.0799309,0.004459536,0.0001345101,0.0004026916,0.0001602036,0.00001413491,0.00265976],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8890497,"threshold_uncertainty_score":0.9986451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03071549913817192,"score_gpt":0.241874325885901,"score_spread":0.2111588267477291,"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."}}