{"id":"W2136539454","doi":"10.1109/ijcnn.2009.5178637","title":"Fast parzen window density estimator","year":2009,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Kernel density estimation; Estimator; Density estimation; Computer science; Kernel (algebra); Smoothing; Variable kernel density estimation; Algorithm; Kernel smoother; Covariance; Gaussian process; Probability density function; Matching (statistics); Nonparametric statistics; Parametric statistics; Kernel method; Mathematics; Artificial intelligence; Statistics; Gaussian; Support vector machine; Radial basis function kernel","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":[],"consensus_categories":[],"category_scores_codex":[0.0000701181,0.0001003997,0.0001082411,0.00003762476,0.0001065319,0.0002131028,0.0006524526,0.00003893695,0.0000488031],"category_scores_gemma":[0.00001745804,0.00007880022,0.00003448808,0.0002370308,0.00001824335,0.0004449552,0.00007428692,0.00007555205,0.0002684438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001201103,"about_ca_system_score_gemma":0.00005958564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008348409,"about_ca_topic_score_gemma":0.000003729532,"domain_scores_codex":[0.9991989,0.00001047956,0.0001210041,0.0002733173,0.000161812,0.0002345179],"domain_scores_gemma":[0.9993932,0.00001486216,0.00003724045,0.0003886965,0.00005276182,0.0001132819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000002668051,0.00007646777,0.002414189,0.00000742046,0.000004575102,0.00004533395,0.0001475914,0.00001539148,0.0007628003,0.7686937,0.004676358,0.2231535],"study_design_scores_gemma":[0.0008628414,0.0005303248,0.465879,0.00006789461,0.00001302681,0.0003029966,0.00004591903,0.08903005,0.03425142,0.4030686,0.004841823,0.001106086],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006478366,0.00002358589,0.9560859,0.004026559,0.00008664514,0.00005400587,2.767781e-7,0.0002645417,0.03298016],"genre_scores_gemma":[0.8502628,0.000002104229,0.1477919,0.001291475,0.00002999188,0.000001061402,3.659253e-7,0.000001932716,0.0006183961],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8437844,"threshold_uncertainty_score":0.3450391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007155436840008174,"score_gpt":0.2237069706154061,"score_spread":0.216551533775398,"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."}}