{"id":"W2344244807","doi":"10.1109/tcyb.2015.2492468","title":"Extreme Learning Machine With Subnetwork Hidden Nodes for Regression and Classification","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extreme learning machine; Subnetwork; Bottleneck; Computer science; Artificial intelligence; Generalization; Artificial neural network; Backpropagation; Machine learning; Support vector machine; Residual; Feedforward neural network; Feed forward; Pattern recognition (psychology); Algorithm; Engineering; Mathematics; Computer network","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":[0.001510631,0.0008611735,0.0009441313,0.001042658,0.0003532202,0.0007395201,0.001024225,0.0009568239,0.001799805],"category_scores_gemma":[0.004839465,0.0003308544,0.0007480882,0.001764609,0.0006596416,0.001095643,0.00094927,0.002056963,0.0009682724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004631957,"about_ca_system_score_gemma":0.0003673401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008418109,"about_ca_topic_score_gemma":0.0009060204,"domain_scores_codex":[0.9991048,0.0003532105,0.00005013083,0.0001408634,0.000302025,0.00004888398],"domain_scores_gemma":[0.9987285,0.0007056688,0.0001205821,0.000233181,0.0001815874,0.00003055285],"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":[0.0001309962,0.0001025461,0.002071864,0.0002452119,0.0001568383,0.0002105475,0.0001084326,0.6093493,0.00709115,0.04211606,0.004693165,0.3337239],"study_design_scores_gemma":[0.00000408139,0.00002185528,0.0002994358,0.000009443454,0.000005810275,0.00003030916,0.000005060992,0.9806002,0.001091594,0.01658371,0.001339963,0.000008561778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007383779,0.0006853698,0.9899005,0.0001574597,0.00004938108,0.00002431284,0.00005659939,0.0006476394,0.001094836],"genre_scores_gemma":[0.3773009,0.001320976,0.6148083,0.0001699971,0.0001865283,0.0002927128,0.0006059681,0.000193757,0.0051209],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001799805,"threshold_uncertainty_score":0.007989109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05160575226254047,"score_gpt":0.2679118930501463,"score_spread":0.2163061407876058,"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."}}