{"id":"W2139902660","doi":"10.1016/j.ins.2012.02.017","title":"A survey of techniques for incremental learning of HMM parameters","year":2012,"lang":"en","type":"article","venue":"Information Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Machine learning; Hidden Markov model; Artificial intelligence; Benchmarking; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001511917,0.00004093268,0.00007531373,0.0001513682,0.0001112192,0.00003545195,0.0003521435,0.00002425344,0.000002975396],"category_scores_gemma":[0.000114869,0.00003465636,0.00002967457,0.0005390045,0.0001135526,0.001958759,0.00006539197,0.00002901846,0.000002444421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001058701,"about_ca_system_score_gemma":0.00003323231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002414296,"about_ca_topic_score_gemma":0.000002596285,"domain_scores_codex":[0.999352,0.00002556615,0.000285345,0.00005026007,0.000178261,0.0001085075],"domain_scores_gemma":[0.9993092,0.0001205709,0.0002939359,0.0001018759,0.0001486483,0.00002573492],"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":[0.0000134516,0.0001145842,0.1036612,0.0000874846,0.00001629436,7.193099e-9,0.0040971,0.0002661647,0.007261405,0.1719878,0.001074342,0.7114202],"study_design_scores_gemma":[0.0001637928,0.0005921639,0.09433095,0.00003008441,0.000004859543,0.000004267205,0.0005881584,0.08698376,0.8049587,0.001286765,0.01082356,0.0002329254],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06332458,0.00001123602,0.9347175,0.00003914323,0.00003276444,0.0001934962,0.000005976665,0.00007438086,0.001600972],"genre_scores_gemma":[0.8682544,0.000004362784,0.1316501,0.00003821891,0.000003092234,0.00004042087,0.000002975478,6.892077e-7,0.000005738141],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8049299,"threshold_uncertainty_score":0.1420053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05495938309666853,"score_gpt":0.3229061009650656,"score_spread":0.2679467178683971,"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."}}