{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004690436,0.0001183209,0.0001469367,0.0002615827,0.00007114255,0.0002845954,0.0003853895,0.00005696463,0.0002789852],"category_scores_gemma":[0.0001487808,0.0001062371,0.00009459951,0.0001274533,0.0000504977,0.0007227806,0.00004612755,0.0002019777,0.0001379591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002483109,"about_ca_system_score_gemma":0.00005827402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007745613,"about_ca_topic_score_gemma":0.000004688874,"domain_scores_codex":[0.9986411,0.0001021941,0.0005468191,0.0001864514,0.000363403,0.0001600622],"domain_scores_gemma":[0.9988025,0.0001131986,0.0003126839,0.0000813713,0.0005659002,0.0001243646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007104434,0.00006978687,0.00003910051,0.000002121143,0.00002956198,0.00005446227,0.001025805,0.0000148628,0.001295847,0.0007348361,0.00005724849,0.9966693],"study_design_scores_gemma":[0.0007235639,0.0008080196,0.0004439027,0.00117896,0.00005831195,0.004489076,0.00911091,0.06163813,0.3318853,0.5768455,0.01166277,0.001155463],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03965712,0.00007165957,0.9568956,0.0009044766,0.001714432,0.00004962297,0.00001323629,0.00002176519,0.0006721175],"genre_scores_gemma":[0.9558908,0.0002314321,0.04252051,0.0009971538,0.0003095972,0.000004253093,0.000006902386,0.000008703678,0.00003067795],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9955138,"threshold_uncertainty_score":0.4332223,"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."}}