{"id":"W2098000447","doi":"10.7551/mitpress/7503.003.0061","title":"A PAC-Bayes Risk Bound for General Loss Functions","year":2007,"lang":"en","type":"book-chapter","venue":"The MIT Press eBooks","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Upper and lower bounds; Bayes' theorem; Set (abstract data type); Bayesian probability; Mathematics; AdaBoost; Exponential function; Statistics; Computer science; Artificial intelligence; Mathematical analysis; Classifier (UML)","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":[0.009454269,0.003317384,0.002787192,0.002357534,0.00149292,0.005557836,0.003860623,0.00396771,0.01047289],"category_scores_gemma":[0.0397346,0.001204599,0.002115985,0.002496341,0.003061557,0.008992258,0.004675527,0.009275153,0.004693043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004461623,"about_ca_system_score_gemma":0.002001519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001553277,"about_ca_topic_score_gemma":0.001267048,"domain_scores_codex":[0.9899704,0.002629289,0.0003868467,0.001516986,0.004786822,0.0007095874],"domain_scores_gemma":[0.9825736,0.0126737,0.0006106842,0.001534152,0.00222465,0.0003832089],"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":[0.0001949239,0.0001509704,0.0008398648,0.0005066416,0.0001273382,0.0002826959,0.0002304187,0.2285645,0.003060731,0.551075,0.02539109,0.1895757],"study_design_scores_gemma":[0.00001542698,0.00007563038,0.0002838902,0.0002057449,0.00004394559,0.0004307526,0.00002419425,0.5978904,0.001948817,0.3838516,0.0151845,0.00004509954],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00185653,0.003423179,0.9796848,0.0007286257,0.0002063701,0.00005888502,0.0001038138,0.0002480814,0.01368976],"genre_scores_gemma":[0.2362747,0.01003992,0.7128512,0.002328322,0.002500916,0.001275057,0.0009448851,0.001338911,0.03244608],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01047289,"threshold_uncertainty_score":0.04999954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03722296304277321,"score_gpt":0.2710257294086874,"score_spread":0.2338027663659141,"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."}}