{"id":"W2149614431","doi":"10.1007/s102080010030","title":"Best Choices for Regularization Parameters in Learning Theory: On the Bias—Variance Problem","year":2002,"lang":"en","type":"article","venue":"Mathematische Annalen","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":258,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematics; Regularization (linguistics); Variance (accounting); Statistics; Econometrics; Applied mathematics; Mathematical optimization; Artificial intelligence; Calculus (dental); Computer science","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":[0.0239187,0.002086495,0.003030858,0.002649605,0.001649851,0.003447472,0.003342186,0.007257114,0.001730596],"category_scores_gemma":[0.08112113,0.001837165,0.001381955,0.001636615,0.005267774,0.007810921,0.005124811,0.007586032,0.0006354686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001776694,"about_ca_system_score_gemma":0.001615384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001633547,"about_ca_topic_score_gemma":0.001530954,"domain_scores_codex":[0.9898165,0.007397019,0.0003440214,0.0009190712,0.001156435,0.0003667831],"domain_scores_gemma":[0.9514127,0.04186726,0.001400503,0.002357326,0.002209626,0.000752621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006624734,0.0002181615,0.001734158,0.0006733895,0.0004111892,0.0001261844,0.0003591157,0.2002022,0.002571525,0.6476317,0.01369287,0.131717],"study_design_scores_gemma":[0.0001112222,0.00005094214,0.0002408563,0.0001807219,0.00005188743,0.00004648765,0.00003838339,0.3928933,0.0007859449,0.6040152,0.001543304,0.00004183475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.023061,0.0055324,0.9611712,0.00631052,0.0001783715,0.00004788774,0.00009222287,0.0003378408,0.003268567],"genre_scores_gemma":[0.4855422,0.005115408,0.4962558,0.00295902,0.001180251,0.0003990013,0.0003622047,0.001536376,0.00664972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0239187,"threshold_uncertainty_score":0.1264957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08291733840487436,"score_gpt":0.2644609902347111,"score_spread":0.1815436518298367,"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."}}