{"id":"W2983445208","doi":"10.1142/s0218213019500209","title":"Incremental Subclass Support Vector Machine","year":2019,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Support vector machine; Computer science; Decision boundary; Classifier (UML); Discriminative model; Artificial intelligence; Convex optimization; Linear classifier; Machine learning; Synthetic data; Regular polygon; Pattern recognition (psychology); Kernel method; Kernel (algebra); Data mining; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005399411,0.0001214165,0.0001762704,0.0002445185,0.00003694514,0.0003749304,0.001377113,0.00005684249,0.001587398],"category_scores_gemma":[0.0001386106,0.0001034304,0.0001605014,0.0001547151,0.0000323164,0.001512853,0.0001847159,0.0002351684,0.001395579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009425191,"about_ca_system_score_gemma":0.0001185001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001731188,"about_ca_topic_score_gemma":0.000006723312,"domain_scores_codex":[0.9980108,0.00005253033,0.0007545917,0.0001844525,0.0008161663,0.0001814626],"domain_scores_gemma":[0.9985771,0.0001420281,0.0004222308,0.0001810365,0.0005759984,0.0001016744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000288872,0.0004117654,0.00163892,0.000008597907,0.000170695,0.0002438951,0.0008210557,0.0008425196,0.1408567,0.05483053,0.002654371,0.797232],"study_design_scores_gemma":[0.0003256224,0.001090666,0.001855277,0.0002183562,0.00001996288,0.0006482289,0.0005697336,0.03648563,0.9009858,0.03524357,0.0220229,0.0005342751],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4256303,0.00007602336,0.5540204,0.006045388,0.009370008,0.0002246521,0.00002905572,0.00006154153,0.004542608],"genre_scores_gemma":[0.9908497,0.00003956597,0.00798423,0.000611499,0.0003305377,0.000001892635,0.00000716079,0.000007576546,0.0001678619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7966978,"threshold_uncertainty_score":0.999382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04618922746386503,"score_gpt":0.3136424866477004,"score_spread":0.2674532591838354,"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."}}