{"id":"W2103376147","doi":"10.21236/ada454989","title":"Fast Rates for Regularized Least-Squares Algorithm","year":2005,"lang":"en","type":"report","venue":"","topic":"Numerical methods in inverse problems","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Office of Naval Research; Defense Advanced Research Projects Agency; Toyota Motor Corporation; McGovern Institute for Brain Research, Massachusetts Institute of Technology; National Institutes of Health; National Science Foundation","keywords":"Algorithm; Least-squares function approximation; Mathematics; Computer science; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009160344,0.001220691,0.001238202,0.001119973,0.0005538297,0.001875439,0.002236617,0.001865929,0.0040725],"category_scores_gemma":[0.04209252,0.0006446111,0.0008730154,0.0008514293,0.002110217,0.003948922,0.003766249,0.003081314,0.001662497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001422202,"about_ca_system_score_gemma":0.001443135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001086643,"about_ca_topic_score_gemma":0.0007601087,"domain_scores_codex":[0.99606,0.001662162,0.0001528362,0.0003929306,0.001491627,0.0002405187],"domain_scores_gemma":[0.9844199,0.01018406,0.0007958503,0.002188078,0.002072255,0.0003398659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000269075,0.00006661502,0.000697789,0.0002270312,0.00007254905,0.0001163425,0.0001884265,0.3997111,0.006912738,0.5175729,0.002601698,0.07156373],"study_design_scores_gemma":[0.00001494186,0.00003073441,0.00007585737,0.00001945601,0.000007070479,0.0000350379,0.000009219779,0.9360619,0.001586573,0.0611918,0.000955508,0.00001191827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.008093667,0.0004305813,0.9889414,0.0003138852,0.00006020485,0.00005397736,0.00004126249,0.0003152208,0.001749762],"genre_scores_gemma":[0.3150494,0.001130603,0.6742353,0.0003724443,0.0002558775,0.0006251354,0.0003352606,0.000758187,0.007237814],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.009160344,"threshold_uncertainty_score":0.04844511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.188788152713903,"score_gpt":0.4656749453262835,"score_spread":0.2768867926123806,"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."}}