{"id":"W4200166130","doi":"10.1109/tcyb.2021.3131424","title":"Learning Performance of Weighted Distributed Learning With Support Vector Machines","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Independent and identically distributed random variables; Computer science; Markov chain; Ergodic theory; Generalization; Benchmark (surveying); Convergence (economics); Generalization error; Sampling (signal processing); Algorithm; Rate of convergence; Divide and conquer algorithms; Artificial intelligence; Machine learning; Mathematics; Artificial neural network; Statistics; Random variable","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":[0.00007859153,0.0001371361,0.0001641345,0.00007759882,0.0001869148,0.00005220214,0.0001816836,0.00007011476,0.0001738901],"category_scores_gemma":[0.000005729762,0.0001192356,0.00006035198,0.0004670173,0.00004244229,0.0002046377,0.00000366695,0.0003939211,0.00005251128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002179913,"about_ca_system_score_gemma":0.0000849226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001172076,"about_ca_topic_score_gemma":0.00001278175,"domain_scores_codex":[0.9989587,0.00008234369,0.00020718,0.000265908,0.0002850528,0.0002008129],"domain_scores_gemma":[0.9993234,0.0000885899,0.00009257552,0.0002260525,0.000195612,0.00007375322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000336029,0.001333701,0.007019227,0.0003075598,0.000292425,0.0001553976,0.003846367,0.4985579,0.07591434,0.0003458154,0.0004955669,0.4113957],"study_design_scores_gemma":[0.0008868672,0.001194821,0.002628809,0.0001998389,0.00004911921,0.00008128057,0.000139445,0.3885674,0.6027657,0.00003114629,0.003113691,0.0003418171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4502939,0.00001141367,0.5488455,0.0001291107,0.0001610318,0.00004816274,0.000008441966,0.0001057124,0.0003968279],"genre_scores_gemma":[0.9898224,0.0001198669,0.008458144,0.00003502898,0.00001453659,0.00001081221,0.0000232631,0.00001332138,0.001502577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5403873,"threshold_uncertainty_score":0.4862285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008895109488162542,"score_gpt":0.2145986807299271,"score_spread":0.2057035712417646,"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."}}