{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004691343,0.0003721488,0.000443973,0.0002394322,0.0009922371,0.0004353552,0.0007614295,0.0001816027,0.00008534319],"category_scores_gemma":[0.0000222702,0.0003866781,0.0002240042,0.0005958995,0.00004197196,0.0005370226,0.0000153382,0.0007300424,0.0002229048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001374797,"about_ca_system_score_gemma":0.000143177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007311586,"about_ca_topic_score_gemma":0.00007782211,"domain_scores_codex":[0.9965919,0.0006378781,0.000551692,0.0009563331,0.000676327,0.0005858305],"domain_scores_gemma":[0.9981602,0.0001840168,0.0001945374,0.001050943,0.000178265,0.0002320534],"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":[0.00002117824,0.0009499379,0.0001513574,0.000379128,0.0002690197,0.0004875579,0.006760033,0.5966787,0.06294486,0.002297369,0.0005743287,0.3284866],"study_design_scores_gemma":[0.001559239,0.0003820604,0.0003514727,0.0006750621,0.00006340827,0.001061292,0.0002827758,0.8814176,0.01152492,0.00008635232,0.1013933,0.001202427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01977347,0.0007735841,0.9729987,0.0004565462,0.002814499,0.0002159142,0.00001705055,0.0009019821,0.002048247],"genre_scores_gemma":[0.9790185,0.0000749883,0.0006730173,0.0001390987,0.00007865479,0.00007903054,0.00001208171,0.00005339109,0.01987124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9723257,"threshold_uncertainty_score":0.9998585,"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."}}