{"id":"W2130539867","doi":"","title":"Generalization Error and Algorithmic Convergence of Median Boosting","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Boosting (machine learning); AdaBoost; Margin (machine learning); Generalization; Generalization error; Computer science; Confidence interval; Algorithm; Extension (predicate logic); Artificial intelligence; Convergence (economics); Mathematics; Pattern recognition (psychology); Machine learning; Statistics; Support vector machine; Artificial neural network","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":[0.01320654,0.0008876387,0.001673697,0.001377398,0.0008031524,0.001611325,0.001775844,0.001735161,0.001726551],"category_scores_gemma":[0.05784245,0.0006382497,0.0009437122,0.001067609,0.00321949,0.00318106,0.0034254,0.003155897,0.0004482387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001520013,"about_ca_system_score_gemma":0.001113271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001077212,"about_ca_topic_score_gemma":0.0008127998,"domain_scores_codex":[0.9956816,0.001807877,0.0001732795,0.000712258,0.001343571,0.0002813388],"domain_scores_gemma":[0.9715385,0.02049119,0.001773874,0.00213179,0.003687726,0.0003770157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000226765,0.00005669162,0.002850851,0.0001679527,0.0001255328,0.00009642521,0.000195096,0.7075251,0.00266045,0.2151466,0.001876873,0.06907172],"study_design_scores_gemma":[0.00001746855,0.00005766614,0.0005259175,0.00003472243,0.00001449263,0.00004912842,0.00001063583,0.8929916,0.001327864,0.1041126,0.0008446182,0.00001324929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01773903,0.0007736372,0.9781162,0.0004871327,0.00004469957,0.00002731708,0.00002985538,0.0002213434,0.002560849],"genre_scores_gemma":[0.7176809,0.001347032,0.274979,0.0006365404,0.0003260826,0.0003287018,0.0002718433,0.0003848551,0.004045134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01320654,"threshold_uncertainty_score":0.06984371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01516538962142985,"score_gpt":0.2459375295064859,"score_spread":0.230772139885056,"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."}}