{"id":"W2050089427","doi":"10.1142/s0218001403002423","title":"A FAST SVM TRAINING ALGORITHM","year":2003,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Chinese Academy of Sciences; Royal Society of Canada","keywords":"MNIST database; Support vector machine; Computer science; Kernel (algebra); Scalability; Artificial intelligence; Algorithm; Machine learning; Generalization; Radial basis function kernel; Test set; Key (lock); Pattern recognition (psychology); Principal component analysis; Kernel method; Deep learning; Mathematics; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001000472,0.0008914053,0.001270011,0.001106238,0.0006580941,0.001152095,0.001524061,0.001457897,0.008137107],"category_scores_gemma":[0.002828937,0.0006164132,0.0008664461,0.001088085,0.0003079327,0.001328619,0.001336306,0.001762209,0.006917062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004881549,"about_ca_system_score_gemma":0.001494953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002208059,"about_ca_topic_score_gemma":0.001919414,"domain_scores_codex":[0.9991724,0.0001424399,0.00006984425,0.0002001546,0.0003071378,0.0001080596],"domain_scores_gemma":[0.9989698,0.0002340804,0.00005911715,0.0001433061,0.0005459677,0.00004769357],"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":[0.0001422485,0.00007489038,0.0005846845,0.00009753975,0.00005272554,0.00007605609,0.00003848062,0.08017546,0.0105274,0.007965705,0.01294922,0.8873156],"study_design_scores_gemma":[0.00003378155,0.00004566678,0.0002284522,0.00001187664,0.00001098071,0.0000858541,0.00001198031,0.9827577,0.004029942,0.004896485,0.007875666,0.00001159394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002275223,0.0001334699,0.9946939,0.00008627473,0.00007953611,0.00005898403,0.00009148983,0.001786645,0.0007943712],"genre_scores_gemma":[0.06636321,0.0002300415,0.9259079,0.0001592916,0.0001400434,0.0003539343,0.001060096,0.0002658865,0.00551956],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008137107,"threshold_uncertainty_score":0.02722132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1072973208037406,"score_gpt":0.3113037660715855,"score_spread":0.2040064452678449,"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."}}