{"id":"W2539488803","doi":"10.1109/acssc.2010.5757506","title":"Empirical risk minimization-based analysis of segmented compressed sampling","year":2010,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Minification; Computer science; Realization (probability); Sampling (signal processing); Compressed sensing; Selection (genetic algorithm); Empirical risk minimization; Operator (biology); Algorithm; Mathematical optimization; Integrator; Shrinkage; SIGNAL (programming language); Quality (philosophy); Artificial intelligence; Mathematics; Statistics; Machine learning; Bandwidth (computing); Computer vision; Telecommunications","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":[0.00168074,0.0007800808,0.0006589278,0.0008233748,0.0002025493,0.0006895685,0.0007618323,0.0006509437,0.001931739],"category_scores_gemma":[0.006964069,0.0002718056,0.0004933954,0.0004609019,0.001419982,0.001339754,0.0008464774,0.0009931745,0.0002008432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007525589,"about_ca_system_score_gemma":0.0006630616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009645604,"about_ca_topic_score_gemma":0.0006046664,"domain_scores_codex":[0.999069,0.0003400996,0.00002685124,0.000104914,0.0004089835,0.00005014706],"domain_scores_gemma":[0.9970948,0.001900337,0.0002647795,0.0001701727,0.0005065518,0.00006329238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001694808,0.00004476521,0.0007724282,0.0002588688,0.00009667164,0.0002448487,0.0002122186,0.5609202,0.022029,0.3606766,0.001656484,0.05291849],"study_design_scores_gemma":[0.000003671549,0.00001952708,0.0001537822,0.000007859912,0.000005028464,0.00004016264,0.00000701672,0.98304,0.001374575,0.01478638,0.0005545223,0.000007505019],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01029052,0.0002239205,0.9879693,0.0001443344,0.00001349108,0.00001513686,0.00002017902,0.0000448566,0.001278398],"genre_scores_gemma":[0.6665701,0.001760353,0.3228289,0.0002609388,0.0002261835,0.0001943993,0.0003072386,0.0002037221,0.007648197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001931739,"threshold_uncertainty_score":0.008888721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02471463068432771,"score_gpt":0.2846953964339063,"score_spread":0.2599807657495786,"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."}}