{"id":"W3159094249","doi":"10.1109/tnnls.2021.3071083","title":"Maximum <i>A Posteriori</i> Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec","keywords":"Hidden Markov model; Maximum a posteriori estimation; Computer science; Artificial intelligence; Pattern recognition (psychology); Inference; Feature (linguistics); A priori and a posteriori; Dirichlet process; Feature selection; Machine learning; Focus (optics); Prior probability; Model selection; Speech recognition; Maximum likelihood; Mathematics; Bayesian probability; Statistics","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":[0.002300274,0.0009935437,0.001343345,0.0006266148,0.0004579177,0.001006319,0.001645402,0.0009620773,0.003263862],"category_scores_gemma":[0.007744316,0.0008249446,0.001701306,0.0009179072,0.001000251,0.001325464,0.001249616,0.002380951,0.001139714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008325061,"about_ca_system_score_gemma":0.001519985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006008634,"about_ca_topic_score_gemma":0.005164743,"domain_scores_codex":[0.9989134,0.0005125106,0.0000530449,0.0002122419,0.000230027,0.00007879658],"domain_scores_gemma":[0.9966974,0.002665115,0.0001717931,0.0001932117,0.0002111419,0.00006127955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001867846,0.0001027823,0.001304992,0.0002244721,0.0001272954,0.0001774677,0.0002146776,0.8017284,0.003884841,0.06300001,0.002761184,0.1262871],"study_design_scores_gemma":[0.000003468403,0.00001123478,0.00007769966,0.000006446957,0.00000389416,0.00001609694,0.000004030038,0.9905211,0.0002963083,0.008676567,0.0003780509,0.000005145283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001975248,0.0001140511,0.9973727,0.00007178382,0.00001217446,0.0000145637,0.00003183571,0.0001509318,0.0002566709],"genre_scores_gemma":[0.3630952,0.0009465799,0.6272418,0.0003191027,0.0001911527,0.0006454935,0.0008426011,0.0004208149,0.006297201],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006008634,"threshold_uncertainty_score":0.01216519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02986876484036317,"score_gpt":0.2442255362818822,"score_spread":0.2143567714415191,"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."}}