{"id":"W4225401927","doi":"10.32473/flairs.v35i.130667","title":"Integration of Multivariate Beta-based Hidden Markov Models and Support Vector Machines with Medical Applications","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hidden Markov model; Discriminative model; Support vector machine; Artificial intelligence; Computer science; Fisher kernel; Pattern recognition (psychology); Kernel (algebra); Generative model; Machine learning; Multivariate statistics; Decision boundary; Kernel method; Generative grammar; Mathematics; Kernel Fisher discriminant analysis","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":[0.001475452,0.0005971421,0.0008197559,0.0009179495,0.0002078635,0.0007060526,0.001087937,0.0009144499,0.001095809],"category_scores_gemma":[0.003815525,0.0004131337,0.0009440018,0.000920567,0.0003850897,0.001462671,0.0007180835,0.00109693,0.0006078986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004492812,"about_ca_system_score_gemma":0.0005472758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002493277,"about_ca_topic_score_gemma":0.002474468,"domain_scores_codex":[0.9992608,0.000254896,0.00004925417,0.0001726836,0.0001926235,0.00006979636],"domain_scores_gemma":[0.9986756,0.0007984209,0.0001324959,0.0001076433,0.0002257637,0.0000600845],"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":[0.0002001514,0.0001868399,0.009549574,0.0001941268,0.000251923,0.0002631351,0.0001699952,0.5917711,0.005398665,0.04005148,0.002762668,0.3492004],"study_design_scores_gemma":[0.000002734443,0.00002062017,0.0003917675,0.000005960464,0.00001275144,0.00003803003,0.000005532215,0.9915171,0.0003304082,0.007093161,0.0005752239,0.000006753176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0115834,0.000756228,0.9861118,0.0002683194,0.00006110445,0.00001638967,0.00005932623,0.0003824762,0.0007608773],"genre_scores_gemma":[0.7935287,0.001526108,0.2000773,0.0002885443,0.0002297726,0.0001013675,0.0003988356,0.0001051055,0.003744278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002493277,"threshold_uncertainty_score":0.007803023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1128507009302343,"score_gpt":0.3615773986869301,"score_spread":0.2487266977566958,"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."}}