{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004524765,0.0002325194,0.0002425508,0.0000953525,0.0002745523,0.000266529,0.0003105311,0.0001350517,6.68931e-7],"category_scores_gemma":[0.00001545972,0.0002087698,0.00006645268,0.0001819027,0.00005253816,0.0011813,0.0000425102,0.0002256112,9.870448e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004532095,"about_ca_system_score_gemma":0.00005358182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004501204,"about_ca_topic_score_gemma":7.658583e-7,"domain_scores_codex":[0.9985636,0.00006353409,0.0001765772,0.000422316,0.0002171528,0.0005567803],"domain_scores_gemma":[0.999302,0.0001509807,0.0001197842,0.0001542582,0.0001019348,0.0001710849],"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":[0.00005643191,0.00005904075,0.00003500243,0.0001604815,0.00002722274,0.000004151256,0.001991357,0.01075596,0.1142257,0.002556005,0.005696909,0.8644318],"study_design_scores_gemma":[0.0003027365,0.00003598067,0.000003169336,0.00004084017,0.00002506909,0.00006318814,0.000004477784,0.9816937,0.004246459,0.01312462,0.0001744082,0.000285295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01000152,0.0005951123,0.9864902,0.002110869,0.0001610245,0.0002850577,0.00000364884,0.0001786693,0.000173849],"genre_scores_gemma":[0.4980132,0.000002585307,0.4993069,0.002259581,0.0002066862,0.00002212366,9.413586e-7,0.00001731013,0.0001706532],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9709378,"threshold_uncertainty_score":0.8513385,"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."}}