{"id":"W3169408724","doi":"10.1155/2021/2026895","title":"An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Artificial intelligence; Computer science; k-nearest neighbors algorithm; Kernel (algebra); Support vector machine; Artificial neural network; Pattern recognition (psychology); Radial basis function kernel; Machine learning; Standard deviation; Kernel method; Data mining; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005519068,0.001022621,0.001380257,0.0008657707,0.0005216482,0.0007189901,0.001535941,0.0007677879,0.002455228],"category_scores_gemma":[0.002284157,0.0004635832,0.001004936,0.001143694,0.000338759,0.001481637,0.0009536126,0.0011973,0.001916726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008492771,"about_ca_system_score_gemma":0.00132503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02530389,"about_ca_topic_score_gemma":0.0162055,"domain_scores_codex":[0.9990564,0.0001455179,0.00007052747,0.0002860653,0.0003382785,0.0001033378],"domain_scores_gemma":[0.9994149,0.0001067762,0.00004689088,0.00008734842,0.0003097745,0.00003426281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003373871,0.0001523911,0.002300643,0.0001845776,0.0001220775,0.0001414411,0.0001197099,0.2005519,0.01234722,0.003787361,0.008602165,0.7713531],"study_design_scores_gemma":[0.000008919327,0.00001863337,0.0003735098,0.000005039502,0.0000092051,0.00005188113,0.00001728399,0.9950787,0.001926125,0.001173414,0.00132365,0.00001367013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01011446,0.0005496251,0.9854114,0.0001007374,0.0001199968,0.00006805782,0.0001653382,0.002297672,0.001172811],"genre_scores_gemma":[0.5091674,0.0009511723,0.4773554,0.0002335254,0.0001058277,0.0003250001,0.002396329,0.0003840416,0.009081241],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02530389,"threshold_uncertainty_score":0.05031323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03501988498422588,"score_gpt":0.301213283837839,"score_spread":0.2661933988536131,"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."}}