{"id":"W2249161032","doi":"10.1109/icdm.2015.127","title":"Ensemble Kernel Mean Matching","year":2015,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kernel (algebra); Benchmark (surveying); Matching (statistics); Partition (number theory); Kernel density estimation; Quadratic equation; Algorithm; Variable kernel density estimation; Test data; Computer science; Mathematics; Kernel method; Statistics; Artificial intelligence; Support vector machine; Combinatorics","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":[0.002140116,0.0009531952,0.002686278,0.001816973,0.0008716326,0.001532186,0.002327282,0.001685024,0.00243845],"category_scores_gemma":[0.007267703,0.0005141757,0.001581684,0.002346451,0.0005988714,0.002806643,0.002440173,0.001639413,0.001504907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007721015,"about_ca_system_score_gemma":0.0012193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002521201,"about_ca_topic_score_gemma":0.002577239,"domain_scores_codex":[0.9979525,0.0004179854,0.0001140804,0.0006914978,0.0006162476,0.0002077128],"domain_scores_gemma":[0.9972386,0.000646515,0.0002727657,0.0009260604,0.0008149775,0.0001011789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002677121,0.0001644918,0.004067208,0.0001001447,0.0002770991,0.00008591622,0.000160554,0.2193897,0.008840504,0.01492075,0.005355284,0.7463706],"study_design_scores_gemma":[0.0000123511,0.00004801999,0.0008320605,0.000007710585,0.00002849753,0.0001129447,0.00002735384,0.9834177,0.004095489,0.009718241,0.001679289,0.00002039445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009439357,0.0002156336,0.9887288,0.0000416024,0.00003377297,0.00002309896,0.00004560449,0.0009578143,0.0005143807],"genre_scores_gemma":[0.380295,0.0003102455,0.6140399,0.0001674728,0.0001121072,0.0001651754,0.0007791118,0.0003694608,0.003761459],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002686278,"threshold_uncertainty_score":0.01131815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04327612217669094,"score_gpt":0.2825820052282778,"score_spread":0.2393058830515868,"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."}}