{"id":"W2037890934","doi":"10.1117/12.853031","title":"Human action recognition using extreme learning machine via multiple types of features","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Action recognition; Extreme learning machine; Artificial intelligence; Action (physics); Machine learning; Pattern recognition (psychology); Artificial neural 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006869067,0.0002412171,0.0003142311,0.000140042,0.0001588666,0.0001133267,0.0009208853,0.0001892941,0.00001245616],"category_scores_gemma":[0.0006403591,0.000208171,0.0003961013,0.0003198212,0.0001296493,0.000671562,0.0002045036,0.0005908424,0.000001150561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005559193,"about_ca_system_score_gemma":0.00002056976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008250413,"about_ca_topic_score_gemma":0.000001561454,"domain_scores_codex":[0.9983058,4.014891e-8,0.0005027365,0.0003538205,0.0005472266,0.0002904024],"domain_scores_gemma":[0.9980702,0.0001119012,0.0005056327,0.00006777993,0.001157394,0.00008706408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002979205,0.0001045009,0.001218557,0.0002709095,0.0001567484,5.777113e-8,0.0002503948,0.000470657,0.924891,0.06731303,0.0002034939,0.005090868],"study_design_scores_gemma":[0.001266875,0.0004139329,0.005722591,0.0002924623,0.0001272256,0.00005191741,0.0003278554,0.6648811,0.3216347,0.003293058,0.001499774,0.0004885065],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964916,0.00003882963,0.00130873,0.0006077737,0.000326812,0.0002289934,0.000008223584,0.0001244533,0.0008646169],"genre_scores_gemma":[0.799131,0.00001163593,0.2003822,0.00002544146,0.0002729888,0.00001953925,0.000006999077,0.00003023509,0.0001200064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6644104,"threshold_uncertainty_score":0.8488969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207807279645398,"score_gpt":0.2537535707021809,"score_spread":0.2316754979057269,"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."}}