{"id":"W2107370115","doi":"10.1109/tnn.2009.2022979","title":"A Multiscale Scheme for Approximating the Quantron's Discriminating Function","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Maxima and minima; Function approximation; Laplace transform; Wavelet; Artificial neural network; Convergence (economics); Function (biology); Multiresolution analysis; Scheme (mathematics); Computer science; Algorithm; Mathematics; Applied mathematics; Approximation theory; Mathematical optimization; Wavelet transform; Artificial intelligence; Mathematical analysis; Discrete wavelet transform","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":[0.0004889846,0.0001719787,0.000157726,0.00006047487,0.0007747771,0.0002454422,0.000418086,0.00007299128,0.000002807582],"category_scores_gemma":[0.0000140873,0.0001228008,0.0001737983,0.0003230157,0.00003562738,0.0004461419,0.000002080356,0.0003746181,0.00000260098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002687996,"about_ca_system_score_gemma":0.00001001726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006651248,"about_ca_topic_score_gemma":0.000004412016,"domain_scores_codex":[0.9986845,0.0001516429,0.0002657664,0.0003415082,0.0001861474,0.0003704003],"domain_scores_gemma":[0.9989145,0.0004931583,0.00009638736,0.0003754208,0.00006178398,0.00005876575],"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":[0.00007702225,0.00008246562,0.000001000031,0.000006504461,0.00001307959,0.000002142203,0.0002508413,0.2517387,0.002909063,0.001054912,0.000241796,0.7436225],"study_design_scores_gemma":[0.0005241841,0.0003101272,0.00009002566,0.00002239878,0.00002261162,0.00001470047,0.00003226137,0.9960495,0.001940264,0.0007606839,0.00008117358,0.0001520948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002510986,0.0000688849,0.9942887,0.00135914,0.001007772,0.0003759242,0.000001378831,0.0002843656,0.000102793],"genre_scores_gemma":[0.8754851,0.000003053238,0.1229294,0.001181154,0.0001829403,0.00005245748,9.629674e-7,0.00001118927,0.0001537461],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8729741,"threshold_uncertainty_score":0.5959038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03025656581598325,"score_gpt":0.2815230047353069,"score_spread":0.2512664389193237,"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."}}