{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003362002,0.00004694905,0.0000469088,0.0000331775,0.00004118213,0.0001277027,0.0004097638,0.00001878387,0.00001007883],"category_scores_gemma":[0.00003673269,0.00003793035,0.00001333888,0.0001133064,0.000006185175,0.0003267924,0.0001232212,0.00006819319,0.0006037216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001529687,"about_ca_system_score_gemma":0.00003307337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002185371,"about_ca_topic_score_gemma":0.00002604946,"domain_scores_codex":[0.9994484,0.00003988517,0.00007858317,0.0001736766,0.0001550863,0.0001043549],"domain_scores_gemma":[0.9994195,0.00002344727,0.00002903883,0.0004027341,0.00003416797,0.00009117863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002094885,0.0000351211,0.001396324,0.000004019007,0.000004195244,0.000004215404,0.002236827,0.0003176051,0.0009019846,0.855624,0.01922898,0.1202447],"study_design_scores_gemma":[0.0008623511,0.0001372736,0.01099165,0.00001568278,0.000005823351,0.00006862012,0.0006291943,0.5319642,0.00234533,0.12871,0.3237299,0.0005399729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006849745,0.00001665915,0.8756363,0.001971023,0.000140623,0.00002546941,1.651e-7,0.0002757623,0.1150843],"genre_scores_gemma":[0.892685,0.000001058008,0.1025701,0.0003843511,0.00004185327,0.000002172542,0.000004631532,0.000003189163,0.004307688],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8858352,"threshold_uncertainty_score":0.7759821,"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."}}