{"id":"W2112338564","doi":"10.1109/tnn.2009.2031143","title":"Semisupervised Least Squares Support Vector Machine","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Support vector machine; Computer science; Heuristics; Generalization; Margin (machine learning); Artificial intelligence; Least squares support vector machine; Machine learning; Classifier (UML); Structured support vector machine; Maximization; Pattern recognition (psychology); Algorithm; Mathematical optimization; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001434044,0.0009133266,0.001614211,0.0006638473,0.0003432624,0.001066418,0.002037406,0.001402777,0.001700156],"category_scores_gemma":[0.008062693,0.0004607469,0.000689064,0.0009055445,0.0006744019,0.001588011,0.001057453,0.001287683,0.00170602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000296468,"about_ca_system_score_gemma":0.000681577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004602837,"about_ca_topic_score_gemma":0.0007382642,"domain_scores_codex":[0.9981607,0.0006953576,0.0001221715,0.0004364087,0.0004953275,0.00009004566],"domain_scores_gemma":[0.9942665,0.002172671,0.0007797639,0.001089202,0.001581357,0.0001105805],"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":[0.0003783708,0.000329184,0.002254766,0.000298999,0.0001760057,0.0001858934,0.0001772742,0.3036375,0.01849812,0.0163604,0.008141498,0.649562],"study_design_scores_gemma":[0.00001181926,0.00005184475,0.0002252263,0.000007776766,0.00000720081,0.00008151036,0.00001730643,0.9887581,0.004399608,0.005453071,0.0009749053,0.00001167848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00900374,0.0001301633,0.9893339,0.00008983956,0.00001982343,0.00003216752,0.0000584513,0.0007167264,0.000615139],"genre_scores_gemma":[0.3521196,0.0002090181,0.6429042,0.0001833471,0.0001115344,0.000238083,0.0008604357,0.0001769767,0.003196859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002037406,"threshold_uncertainty_score":0.007584035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01396795389834953,"score_gpt":0.2336830820277411,"score_spread":0.2197151281293916,"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."}}