{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007900755,0.0002062477,0.0001728791,0.0001074758,0.0002651855,0.0001342872,0.0004285999,0.0001154991,0.0002587966],"category_scores_gemma":[0.000001396526,0.0001813185,0.0001515236,0.000366847,0.00002391006,0.0004966504,0.000001879571,0.0003976378,0.00009963053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002354681,"about_ca_system_score_gemma":0.00001588905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001161886,"about_ca_topic_score_gemma":0.00001220108,"domain_scores_codex":[0.9986952,0.00006986377,0.0002324064,0.0003942652,0.0002448181,0.0003634269],"domain_scores_gemma":[0.9992611,0.0000643894,0.00004651545,0.0004123081,0.00004694263,0.0001687049],"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":[0.0001234106,0.000455619,0.00001480925,0.00001094705,0.00002367108,0.00006567801,0.0002279919,0.2799369,0.004971825,0.0001005975,0.007364595,0.706704],"study_design_scores_gemma":[0.0005865935,0.0004995763,0.0003627838,0.00003592397,0.00001402212,0.00004727786,0.000009569719,0.9883235,0.009011179,0.0001268043,0.0006935198,0.0002892837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01324184,0.000056934,0.9818906,0.002452628,0.001179234,0.0001797849,0.000009992738,0.0004323809,0.0005566229],"genre_scores_gemma":[0.9957497,0.00005803931,0.001063126,0.002560709,0.0001097214,0.00001709575,0.00000792526,0.00001143274,0.000422292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9825078,"threshold_uncertainty_score":0.7393954,"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."}}