{"id":"W2138041254","doi":"10.1109/tsmcb.2002.1049608","title":"Generalized pursuit learning schemes: new families of continuous and discretized learning automata","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":158,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Action (physics); Learning automata; Discretization; Computer science; Scheme (mathematics); Artificial intelligence; Estimator; Algorithm; Automaton; Machine learning; Mathematical optimization; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004522724,0.0004656551,0.0007229263,0.0002836389,0.0003860286,0.0004884303,0.0005274648,0.000213215,0.00007368338],"category_scores_gemma":[0.00003380921,0.0004450493,0.0001431374,0.0003785773,0.0002256377,0.000199657,0.00002997625,0.000767273,0.00005297199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002985034,"about_ca_system_score_gemma":0.0000311005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000600879,"about_ca_topic_score_gemma":0.00002326478,"domain_scores_codex":[0.9968707,0.0004225138,0.0007406412,0.0007808884,0.0006352651,0.0005499944],"domain_scores_gemma":[0.9983394,0.0002613612,0.0003546793,0.0005694779,0.0001082005,0.0003668958],"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":[0.0002036278,0.001313779,0.004604693,0.00121759,0.001262191,0.0001490651,0.02746672,0.1421283,0.008868898,0.0319346,0.005465541,0.775385],"study_design_scores_gemma":[0.003702391,0.001113475,0.0005603384,0.0004984172,0.0001501963,0.000179685,0.0007275753,0.8848,0.00185115,0.0001369797,0.1053266,0.0009532051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2937404,0.004363579,0.691874,0.0007612052,0.001557406,0.0008275563,0.00001691572,0.0009248275,0.005934108],"genre_scores_gemma":[0.9540469,0.002570115,0.005138693,0.00005344396,0.00014563,0.00003147321,0.000004151961,0.00005674278,0.03795286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7744318,"threshold_uncertainty_score":0.9998001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679405674685146,"score_gpt":0.2287138098760468,"score_spread":0.2119197531291953,"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."}}