{"id":"W3048868447","doi":"10.1016/j.neunet.2020.07.036","title":"SVM-Boosting based on Markov resampling: Theory and algorithm","year":2020,"lang":"en","type":"article","venue":"Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Boosting (machine learning); AdaBoost; Support vector machine; Resampling; Artificial intelligence; Markov chain; Computer science; Machine learning; Algorithm; Pattern recognition (psychology); Gradient boosting; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004558188,0.0005230518,0.002306482,0.001034399,0.0006839872,0.001075604,0.002016554,0.001339975,0.00219289],"category_scores_gemma":[0.007802772,0.0008100723,0.001164942,0.001192141,0.0009247335,0.001548647,0.001430962,0.001640809,0.0008875136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008348604,"about_ca_system_score_gemma":0.001262003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002501491,"about_ca_topic_score_gemma":0.002648868,"domain_scores_codex":[0.9981457,0.0009176285,0.00007925215,0.0002309912,0.0004852283,0.0001412075],"domain_scores_gemma":[0.9966848,0.001996539,0.0001825142,0.0003427759,0.000671754,0.0001216045],"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":[0.0003466825,0.00019854,0.001924803,0.0002656719,0.0002250411,0.00007807802,0.0001057514,0.301121,0.006389505,0.123337,0.01017215,0.5558358],"study_design_scores_gemma":[0.000008414729,0.00003545339,0.0001825438,0.000008477508,0.00001388912,0.00004078882,0.000003428622,0.9844598,0.0007009536,0.01356482,0.0009725472,0.000008913019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003225722,0.000471801,0.9954334,0.0001109923,0.00008372617,0.00002793821,0.00001697307,0.0002267698,0.0004026828],"genre_scores_gemma":[0.289828,0.001006207,0.703967,0.0002914812,0.0005066976,0.000221556,0.0002504539,0.0001798841,0.003748727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004558188,"threshold_uncertainty_score":0.02410632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036005395713799,"score_gpt":0.2371297062089851,"score_spread":0.2167696522518471,"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."}}