{"id":"W2026829374","doi":"10.1007/s11760-012-0404-3","title":"SN-SVM: a sparse nonparametric support vector machine classifier","year":2012,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor; Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Artificial intelligence; Pattern recognition (psychology); Computer science; Nonparametric statistics; Relevance vector machine; Classifier (UML); Kernel (algebra); Machine learning; Kernel method; Structured support vector machine; Quadratic classifier; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004305005,0.0002745099,0.0002655572,0.0002539556,0.0001556783,0.0002727971,0.000111609,0.0001218997,0.0001281444],"category_scores_gemma":[0.0001142211,0.0002614254,0.00005708832,0.0005315604,0.000113066,0.001231313,0.00003924376,0.0003166417,0.0001577008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008064946,"about_ca_system_score_gemma":0.00004005891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008181274,"about_ca_topic_score_gemma":0.000001465754,"domain_scores_codex":[0.9985456,0.00004302523,0.0003450487,0.0002669579,0.0002423761,0.0005569531],"domain_scores_gemma":[0.9992764,0.00008828525,0.0000846005,0.0002135744,0.00009919138,0.0002379358],"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":[0.0000261577,0.00008544054,0.00233699,0.0004765652,0.00003937084,0.00002827993,0.000736061,0.00007015404,0.641358,0.00002632924,0.003613906,0.3512027],"study_design_scores_gemma":[0.001768212,0.0001617907,0.06571031,0.0004355584,0.0003485235,0.0006047898,0.0003529531,0.5895655,0.3056017,0.0004169845,0.03295551,0.002078095],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2101936,0.01167755,0.7359845,0.0006385673,0.0008327726,0.0006857648,0.00002887703,0.001636778,0.03832162],"genre_scores_gemma":[0.9851484,0.00006286448,0.01380996,0.0001564325,0.0003480812,0.000007698352,0.00002361464,0.00007998107,0.0003629742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7749549,"threshold_uncertainty_score":0.9999838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02273592347880754,"score_gpt":0.25463939849288,"score_spread":0.2319034750140725,"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."}}