{"id":"W4394586022","doi":"10.1109/tmm.2024.3375774","title":"Progressive Learning Model for Big Data Analysis Using Subnetwork and Moore-Penrose Inverse","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; Vector Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subnetwork; Computer science; Moore–Penrose pseudoinverse; Big data; Theoretical computer science; Artificial intelligence; Inverse; Algorithm; Data mining; Computer 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003866447,0.000180693,0.0002134829,0.0003970527,0.0003470557,0.0003007779,0.0004714793,0.00008756961,0.000007572761],"category_scores_gemma":[0.00002114797,0.00016585,0.0001132169,0.0009294144,0.00006459496,0.0004254506,0.00001319208,0.0003446058,0.0000128562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003410569,"about_ca_system_score_gemma":0.00008868185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008041508,"about_ca_topic_score_gemma":0.00008705005,"domain_scores_codex":[0.9985211,0.00007646884,0.0001991781,0.0007042382,0.0002040081,0.000294933],"domain_scores_gemma":[0.9988545,0.0003130208,0.0000512787,0.0005983106,0.00004531962,0.000137546],"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":[0.00001119245,0.00003980634,0.00004844879,0.00002937498,0.0002331103,0.000011274,0.001509771,0.7361066,0.0001118056,0.00001246939,0.00009379764,0.2617924],"study_design_scores_gemma":[0.0002624169,0.00004331762,0.00003013314,0.00004343426,0.0003512427,0.000008332384,0.00003121179,0.9984728,0.000125767,0.00008040331,0.0003609172,0.0001900848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01394667,0.0002917768,0.9841853,0.0001894955,0.0007941893,0.0002019075,0.00004328134,0.0003296809,0.00001773506],"genre_scores_gemma":[0.8098154,0.00002981057,0.1893254,0.00004761847,0.0001605131,0.00002914892,0.00002616437,0.00002187938,0.0005441023],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7958688,"threshold_uncertainty_score":0.6763168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07840007998234619,"score_gpt":0.323889116765693,"score_spread":0.2454890367833468,"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."}}