{"id":"W2154332623","doi":"10.1109/icpr.2008.4761091","title":"Help-training for semi-supervised discriminative classifiers. Application to SVM","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Artificial intelligence; Support vector machine; Computer science; Co-training; Machine learning; Classifier (UML); Generative grammar; Pattern recognition (psychology); Training set; Semi-supervised learning; Random subspace method; Labeled data; Supervised learning; Training (meteorology); Artificial neural network","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":[0.00224443,0.0007455333,0.0006719186,0.0007303074,0.0004266562,0.0005765815,0.001462995,0.00129364,0.002452245],"category_scores_gemma":[0.007075775,0.0004943219,0.0006053068,0.0005744497,0.0009605551,0.001132469,0.001296087,0.001324673,0.0012081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003677396,"about_ca_system_score_gemma":0.0004433366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006670068,"about_ca_topic_score_gemma":0.001452839,"domain_scores_codex":[0.9983138,0.0009686794,0.00006803688,0.0002688571,0.0003115807,0.00006901188],"domain_scores_gemma":[0.9958134,0.002441641,0.0002554144,0.0007270903,0.0006159189,0.000146464],"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":[0.0002681375,0.0002872066,0.003812202,0.0003715904,0.0001410259,0.0002226681,0.0003438616,0.1581772,0.03161934,0.02110093,0.007914174,0.7757416],"study_design_scores_gemma":[0.00001038166,0.0000654926,0.0004287025,0.00001650012,0.00001011594,0.000143673,0.0000150533,0.9830996,0.007269125,0.006526359,0.002403957,0.00001108315],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007436991,0.0001873632,0.9903453,0.00009446407,0.00002098084,0.00005490684,0.00002349921,0.00115532,0.0006812579],"genre_scores_gemma":[0.3967317,0.00017195,0.599363,0.0002478831,0.00009652119,0.0002406559,0.0002567558,0.0002677472,0.002623722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002452245,"threshold_uncertainty_score":0.01186979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.159852228198708,"score_gpt":0.3218316136727785,"score_spread":0.1619793854740705,"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."}}