{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002017396,0.0001082558,0.000100905,0.00002593966,0.0001352984,0.0001254047,0.000231026,0.00006279355,0.00002055736],"category_scores_gemma":[0.00007728033,0.00009080326,0.00003438783,0.0001727651,0.00002325654,0.0001614579,0.0001225015,0.0002344881,0.00001111875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004996761,"about_ca_system_score_gemma":0.00000718207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001679669,"about_ca_topic_score_gemma":1.895665e-7,"domain_scores_codex":[0.9990727,0.0001316132,0.0001213369,0.0003249808,0.0001411113,0.000208247],"domain_scores_gemma":[0.9991974,0.0004270876,0.00004748845,0.0001676666,0.00002387297,0.0001364882],"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":[0.00005528162,0.00001368971,0.000148933,0.000007669218,0.000003134096,0.00002899627,0.0001004158,0.02218199,0.0001439406,0.0005276214,0.003505005,0.9732833],"study_design_scores_gemma":[0.0002400069,0.0001101148,0.0003231834,0.00003460673,0.00000271036,0.000002797384,0.00001041653,0.997637,0.0001751167,0.0004860397,0.0008674404,0.000110515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006814525,0.0001294852,0.9877706,0.003647854,0.0002993177,0.0001142691,0.00000165749,0.0002137318,0.001008606],"genre_scores_gemma":[0.9472944,0.00001713881,0.03390519,0.01831335,0.0004016031,0.000009277032,0.000008750202,0.00001332223,0.00003699461],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.975455,"threshold_uncertainty_score":0.3702849,"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."}}