{"id":"W2745527096","doi":"10.1016/j.neucom.2017.02.103","title":"Extreme learning machines with heterogeneous data types","year":2017,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Extreme learning machine; Computer science; Machine learning; Artificial intelligence; Simplicity; Data mining; Homogeneous; Data type; Artificial neural network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006946874,0.0007130944,0.001543607,0.001158439,0.0007088417,0.002888816,0.00252907,0.002219954,0.002954375],"category_scores_gemma":[0.02576625,0.0006647339,0.001488516,0.002603444,0.00162698,0.005578211,0.0036586,0.003097287,0.0006310513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006413577,"about_ca_system_score_gemma":0.0004921934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003148928,"about_ca_topic_score_gemma":0.0003455379,"domain_scores_codex":[0.9963832,0.001624693,0.0003046714,0.0006227617,0.0008170928,0.0002475813],"domain_scores_gemma":[0.9883044,0.007647584,0.0007122648,0.002222309,0.0008362801,0.0002771878],"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":[0.0003780632,0.0002647924,0.004799783,0.0002084841,0.0002613048,0.0006928495,0.0001700465,0.625743,0.001450549,0.1939075,0.005270424,0.1668532],"study_design_scores_gemma":[0.00002355538,0.00003544988,0.0003206044,0.0000128891,0.00001606357,0.00009385159,0.00002186054,0.8200244,0.0005912863,0.1780663,0.0007838027,0.00001006339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02762018,0.0002153842,0.9697635,0.0006084769,0.00008602777,0.00003803175,0.0001166059,0.0001966521,0.001355086],"genre_scores_gemma":[0.6627,0.0004490541,0.3279844,0.0004753793,0.0004402011,0.000283708,0.0007970066,0.0001410537,0.006729196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006946874,"threshold_uncertainty_score":0.03673905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05530434169005331,"score_gpt":0.2861630547382647,"score_spread":0.2308587130482114,"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."}}