{"id":"W4776547","doi":"10.1007/978-3-642-21043-3_46","title":"Extending AdaBoost to Iteratively Vary Its Base Classifiers","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; AdaBoost; Artificial intelligence; Base (topology); Pattern recognition (psychology); Machine learning; Support vector machine; Mathematics","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":[0.002373175,0.001016432,0.001465492,0.0008970761,0.0006751096,0.001220003,0.002112666,0.001447474,0.001684027],"category_scores_gemma":[0.003556427,0.0007285491,0.001085277,0.0009612459,0.000582888,0.001424112,0.001361465,0.002317474,0.001521055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006362594,"about_ca_system_score_gemma":0.001053273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004558746,"about_ca_topic_score_gemma":0.00550206,"domain_scores_codex":[0.9989077,0.0002579524,0.00006709386,0.0002705861,0.0004168144,0.00007993782],"domain_scores_gemma":[0.9986284,0.000371672,0.00006832535,0.0002788601,0.0006018084,0.00005105959],"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":[0.0002042237,0.0002550988,0.0008969781,0.00008248818,0.0001665152,0.0000413579,0.00007229184,0.167834,0.01620168,0.00512844,0.004968513,0.8041484],"study_design_scores_gemma":[0.00001277388,0.00004340172,0.0001574219,0.000008849463,0.00003302685,0.00004415407,0.000006933283,0.9871299,0.005625096,0.003533819,0.003390136,0.00001456527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005990301,0.0001852748,0.9910994,0.00005389932,0.0001440694,0.00003330231,0.00001728558,0.001285384,0.001191111],"genre_scores_gemma":[0.1349366,0.0001554096,0.859933,0.000206963,0.0001151915,0.00009625703,0.000141397,0.0006453735,0.003769773],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004558746,"threshold_uncertainty_score":0.01255071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04063029010037732,"score_gpt":0.271457159147261,"score_spread":0.2308268690468837,"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."}}