{"id":"W3136996429","doi":"10.1109/tcsii.2021.3067014","title":"Dynamic Quaternion Extreme Learning Machine","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"National Natural Science Foundation of China","keywords":"Extreme learning machine; Hypercomplex number; Generalization; Computer science; Benchmark (surveying); Quaternion; Artificial neural network; Basis (linear algebra); Early stopping; Network architecture; Artificial intelligence; Algorithm; Mathematics","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":[0.0006001582,0.0004920173,0.0005578984,0.0003444134,0.0002926448,0.000680713,0.0009447141,0.0007192183,0.001963331],"category_scores_gemma":[0.001423066,0.0002370338,0.0004381943,0.0004817098,0.0005643387,0.0009827371,0.000802877,0.0008084814,0.0004483708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005220123,"about_ca_system_score_gemma":0.0003566425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001115815,"about_ca_topic_score_gemma":0.0009391056,"domain_scores_codex":[0.9997224,0.00008284361,0.00001485602,0.0000758579,0.00007528414,0.00002867658],"domain_scores_gemma":[0.9997577,0.00007448999,0.00004207918,0.0000393867,0.0000694631,0.00001687604],"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":[0.00005331765,0.00002292656,0.0007428061,0.00005816326,0.00003786517,0.00009366681,0.00004546491,0.8725098,0.003913862,0.0390306,0.002349456,0.08114206],"study_design_scores_gemma":[0.000002319296,0.000008973912,0.00005770115,0.000002315617,0.000001747641,0.00001259973,0.00000206387,0.9935819,0.0003701161,0.0054813,0.0004754329,0.00000347923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01411584,0.0002653275,0.981653,0.0002092815,0.00006847841,0.00002228798,0.0000465091,0.0002565274,0.00336278],"genre_scores_gemma":[0.7837241,0.0005137288,0.2070478,0.0003112699,0.00009330484,0.0001521411,0.0002471353,0.00008139574,0.007829085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001963331,"threshold_uncertainty_score":0.006567955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01910890022535728,"score_gpt":0.2376184011308976,"score_spread":0.2185095009055403,"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."}}