{"id":"W2035914573","doi":"10.1109/smc.2013.325","title":"Truncation Error Compensation in Kernel Machines","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Canada Research Chairs","keywords":"Computer science; Kernel (algebra); Benchmark (surveying); Series (stratigraphy); Truncation (statistics); Time series; Compensation (psychology); Algorithm; Mean squared error; Artificial intelligence; Data mining; Machine learning; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002202285,0.00043941,0.000744164,0.0005510295,0.0004320875,0.0006706777,0.0008623177,0.0008433769,0.001049123],"category_scores_gemma":[0.01312052,0.0002551957,0.0003367474,0.0006523706,0.0007941463,0.001126923,0.0008520965,0.0008607095,0.000473159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000584814,"about_ca_system_score_gemma":0.0009762341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002797514,"about_ca_topic_score_gemma":0.001693785,"domain_scores_codex":[0.9989868,0.0003965341,0.00006770875,0.0001413547,0.0003243481,0.00008318539],"domain_scores_gemma":[0.9958793,0.002129026,0.0002588119,0.0007010878,0.0009400068,0.00009183397],"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":[0.0005080863,0.00006847362,0.001332947,0.0001075493,0.00004648864,0.0001218223,0.0001734028,0.6959413,0.01345727,0.0331397,0.001881768,0.2532213],"study_design_scores_gemma":[0.000004335713,0.00002112343,0.00009314525,0.000002499578,0.000001714079,0.00001062137,0.000004189727,0.9945624,0.00195107,0.003093835,0.0002503252,0.000004656685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02764047,0.0002337105,0.9710227,0.00008399998,0.00005267077,0.00001674167,0.00001760374,0.0004476495,0.0004843417],"genre_scores_gemma":[0.6958883,0.0002750898,0.3003914,0.0000773573,0.00006602538,0.0000751331,0.000114252,0.0001335852,0.002978764],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002797514,"threshold_uncertainty_score":0.01164693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584187087603315,"score_gpt":0.242344938447053,"score_spread":0.2265030675710199,"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."}}