{"id":"W2997999303","doi":"10.1609/aaai.v34i04.6182","title":"Safe Sample Screening for Robust Support Vector Machine","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Benchmark (surveying); Computer science; Solver; Sample (material); Generalization; Mathematical optimization; Support vector machine; Convex optimization; Machine learning; Artificial intelligence; Regular polygon; 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":[0.006180972,0.001945061,0.001764863,0.001432443,0.0009699215,0.001720095,0.002612464,0.002169327,0.003089075],"category_scores_gemma":[0.03659346,0.0009951475,0.001639811,0.0009810517,0.002229091,0.002753266,0.003679341,0.003618118,0.001283571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008660091,"about_ca_system_score_gemma":0.003037726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001866407,"about_ca_topic_score_gemma":0.00161021,"domain_scores_codex":[0.9935236,0.002561549,0.0004220316,0.0008976044,0.002138775,0.0004565024],"domain_scores_gemma":[0.9845637,0.009577887,0.001134342,0.001832661,0.002469704,0.0004216291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006066126,0.0002093512,0.003359027,0.0003946671,0.0001481751,0.0005316241,0.0002541349,0.5930447,0.01349459,0.06365458,0.008081537,0.316221],"study_design_scores_gemma":[0.00001804035,0.00005621881,0.00009589654,0.00001384845,0.000006288398,0.00003763049,0.00001086244,0.9822209,0.003649565,0.01327529,0.0006036554,0.00001173859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00484929,0.0001361143,0.9931144,0.0001427095,0.00001901653,0.0000715503,0.00003964431,0.001176863,0.0004503968],"genre_scores_gemma":[0.3227842,0.0002799781,0.6724668,0.0004966934,0.0001089691,0.0006704045,0.0006456827,0.0008455755,0.001701746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006180972,"threshold_uncertainty_score":0.0326885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1738317798494326,"score_gpt":0.3038440701750817,"score_spread":0.1300122903256491,"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."}}