{"id":"W1498435869","doi":"10.1007/11551263_10","title":"On Utilizing Stochastic Learning Weak Estimators for Training and Classification of Patterns with Non-stationary Distributions","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor; Carleton University","funders":"","keywords":"Estimator; Computer science; Multinomial distribution; Convergence (economics); Variance (accounting); Artificial intelligence; Distribution (mathematics); Pattern recognition (psychology); Mathematics; Applied 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":[0.008417541,0.001331379,0.002169807,0.001684105,0.0006650422,0.002081394,0.00276076,0.002520007,0.001729758],"category_scores_gemma":[0.02803485,0.0009491587,0.001327552,0.002073976,0.001970745,0.004239571,0.00387568,0.002527103,0.0009198568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005828831,"about_ca_system_score_gemma":0.001086664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002070169,"about_ca_topic_score_gemma":0.002630562,"domain_scores_codex":[0.9976554,0.001004884,0.0002623062,0.0003753277,0.0005746217,0.000127318],"domain_scores_gemma":[0.981753,0.01364014,0.0006647083,0.001740051,0.001932603,0.0002694654],"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":[0.0003851261,0.0001796873,0.003810801,0.0002393096,0.0002100921,0.0001097686,0.0002095894,0.2473475,0.0117888,0.05628175,0.003583591,0.675854],"study_design_scores_gemma":[0.00001313584,0.0000514244,0.0003421165,0.00001730634,0.00002212527,0.00003987843,0.00001527683,0.9698111,0.001903658,0.02704873,0.0007230749,0.00001218279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003934304,0.0001854279,0.9953915,0.0000826414,0.00002444621,0.00002242823,0.00001918157,0.000169935,0.0001701179],"genre_scores_gemma":[0.1223771,0.0007359903,0.8729065,0.0004081493,0.0002605112,0.000225635,0.0005180551,0.0002059364,0.002362092],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008417541,"threshold_uncertainty_score":0.0445168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02172019242159044,"score_gpt":0.2710095178746387,"score_spread":0.2492893254530482,"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."}}