{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003258554,0.0001256498,0.0002555728,0.0001280839,0.0001338635,0.00008982191,0.0001421002,0.00006247361,0.00000286545],"category_scores_gemma":[0.00003057407,0.0001313006,0.0001144526,0.0001874743,0.00002025084,0.001384365,0.000003728843,0.0001469154,7.179802e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005710967,"about_ca_system_score_gemma":0.0001266437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004354142,"about_ca_topic_score_gemma":0.00007637087,"domain_scores_codex":[0.9987977,0.00006629789,0.0004281483,0.0002785054,0.000277854,0.0001515313],"domain_scores_gemma":[0.9983122,0.00008845244,0.0004993608,0.0001762977,0.0007998153,0.0001238696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001325911,0.0003487559,0.00007854895,0.00006925796,0.00003696719,0.0000231114,0.002803478,0.04225854,0.6582671,0.0003408122,0.000007479305,0.2956333],"study_design_scores_gemma":[0.009032522,0.001514261,0.08585459,0.0006709248,0.0002157487,0.0003279523,0.001800086,0.2960615,0.5828152,0.02056477,0.0001664028,0.0009759821],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5239198,0.00002424155,0.475621,0.0001160271,0.0001584737,0.0001110051,0.00001811821,0.00001866056,0.00001261082],"genre_scores_gemma":[0.9589155,0.00001630695,0.04087695,0.00004086428,0.00007787491,0.00001457369,0.00002774741,0.00001215405,0.00001800445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4349957,"threshold_uncertainty_score":0.5354283,"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."}}