{"id":"W2113075278","doi":"10.1109/icip.2009.5414312","title":"Optimum kernel function design from scale space features for object detection","year":2009,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Quadratic programming; Mathematics; Kernel (algebra); Orthant; Pattern recognition (psychology); Kernel method; Radial basis function kernel; Artificial intelligence; Mathematical optimization; Support vector machine; Computer science; Algorithm; Applied mathematics","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":[0.00009646512,0.0001329983,0.0001085031,0.00007107653,0.00006812724,0.00007610051,0.00005253395,0.0001195916,0.00001452806],"category_scores_gemma":[0.00004227112,0.0001314934,0.00005602552,0.0001211605,0.00001062191,0.000177831,0.000003005629,0.00009861828,0.00004509454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001075753,"about_ca_system_score_gemma":0.000007560162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000225625,"about_ca_topic_score_gemma":0.00002381506,"domain_scores_codex":[0.9993905,0.00001985403,0.0001236442,0.0001972905,0.00009696132,0.0001717281],"domain_scores_gemma":[0.9995776,0.00007937812,0.00002532844,0.0002189617,0.00005400392,0.00004475649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006699276,0.00001235432,0.00000545567,0.000006376294,0.00001660806,3.666952e-7,0.00006511837,0.0472683,0.8221188,0.00001880531,0.006297983,0.1241229],"study_design_scores_gemma":[0.0003063034,0.0001043741,0.01963202,0.00001200453,0.0000331658,0.000003560109,0.00004659007,0.4892471,0.4880463,0.001251649,0.001139464,0.0001774512],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02249913,0.0001107289,0.9726934,0.0001640156,0.0005085999,0.0003268722,0.000002361086,0.0007926295,0.002902292],"genre_scores_gemma":[0.8970311,0.00001278407,0.1017138,0.0000540111,0.0002272163,0.000005942377,0.00001778792,0.00002837231,0.0009089489],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.874532,"threshold_uncertainty_score":0.5362147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01481282758066268,"score_gpt":0.2149939842158387,"score_spread":0.200181156635176,"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."}}