{"id":"W2085825224","doi":"10.1109/lsp.2012.2190280","title":"Simultaneous Feature and Model Selection for Continuous Hidden Markov Models","year":2012,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Hidden Markov model; Feature selection; Artificial intelligence; Markov model; Pattern recognition (psychology); Model selection; Selection (genetic algorithm); Maximum-entropy Markov model; Feature (linguistics); Markov process; Data modeling; Markov chain; Machine learning; Variable-order Markov model; Mathematics; Statistics","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":[0.002455855,0.001295556,0.001681962,0.001108325,0.000668928,0.001046317,0.001844118,0.001192977,0.001381769],"category_scores_gemma":[0.006870774,0.0009146368,0.001567088,0.001192698,0.0008214497,0.001529705,0.001838741,0.001708922,0.0004722747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006984486,"about_ca_system_score_gemma":0.001094058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00308538,"about_ca_topic_score_gemma":0.003145224,"domain_scores_codex":[0.9978679,0.0008981256,0.00009947285,0.0004115025,0.0005603515,0.0001625358],"domain_scores_gemma":[0.996618,0.002548468,0.000215069,0.0002639192,0.0002666854,0.00008785832],"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":[0.0003313288,0.0001438761,0.002639516,0.0002572702,0.0004099276,0.0005089082,0.0002729735,0.6217093,0.01163481,0.05598715,0.002899145,0.3032059],"study_design_scores_gemma":[0.0000102461,0.00002654219,0.0001546411,0.00000436355,0.00001603864,0.00004263525,0.00000559121,0.9863871,0.0008507243,0.01204574,0.000444391,0.00001195145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003735763,0.0001588621,0.9956768,0.00006730804,0.00001645516,0.00001405801,0.00002530535,0.0001561896,0.000149282],"genre_scores_gemma":[0.5203503,0.0004824226,0.4759637,0.0002311134,0.0001734514,0.0003070722,0.0006013533,0.0001756929,0.001714909],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00308538,"threshold_uncertainty_score":0.01298797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713981680922567,"score_gpt":0.2578869165525539,"score_spread":0.2407470997433283,"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."}}