{"id":"W1516580029","doi":"10.1109/bigdatacongress.2015.16","title":"A GPU Based SVM Method with Accelerated Kernel Matrix Calculation","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; CUDA; Kernel (algebra); Graphics processing unit; Support vector machine; Parallel computing; Massively parallel; General-purpose computing on graphics processing units; Computation; Matrix multiplication; Coprocessor; Graphics; Computational science; Algorithm; Artificial intelligence; Computer graphics (images); Mathematics","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":[0.0002591998,0.00008525481,0.00008808955,0.00007477724,0.00004513668,0.0001355547,0.0002088314,0.00004944616,0.00006200196],"category_scores_gemma":[0.00001876232,0.00005750799,0.0000211384,0.0003493092,0.00000819396,0.000479322,0.00004488619,0.00005885243,0.0002124589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000234292,"about_ca_system_score_gemma":0.000102125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001115418,"about_ca_topic_score_gemma":0.00001201542,"domain_scores_codex":[0.9991846,0.00008152108,0.000113325,0.0002304659,0.0002485182,0.0001415648],"domain_scores_gemma":[0.9993679,0.00004062868,0.00004631422,0.0002406602,0.0001832824,0.0001211935],"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":[0.0008850145,0.001306412,0.01232774,0.0001339878,0.0001725812,0.0002464169,0.004429336,0.1153026,0.07308151,0.04451957,0.3441362,0.4034586],"study_design_scores_gemma":[0.00103044,0.0001061231,0.0008151708,0.00002133235,0.000005541172,0.000009547231,0.00004512873,0.9614199,0.03193728,0.0007541114,0.003689486,0.0001659266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01107942,0.000009755841,0.981176,0.001015903,0.00007825092,0.0001146812,7.551724e-7,0.0002373513,0.006287918],"genre_scores_gemma":[0.3719307,4.093019e-7,0.6259686,0.0006507559,0.00002305333,0.00001575118,0.00001247673,0.000006227817,0.001391931],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8461173,"threshold_uncertainty_score":0.27308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05919753100313425,"score_gpt":0.330445069901932,"score_spread":0.2712475388987978,"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."}}