{"id":"W2154793733","doi":"10.1109/icpr.2006.586","title":"Function Dot Product Kernels for Support Vector Machine","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Dot product; Support vector machine; Computer science; Relevance vector machine; Product (mathematics); Function (biology); Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001208216,0.00007158548,0.00006793273,0.00004418235,0.00007467999,0.00007034856,0.0001548203,0.00002464335,0.0002036248],"category_scores_gemma":[0.00001022708,0.00005473111,0.00004421654,0.000105274,0.000007650657,0.0003415318,0.00003859922,0.00003412794,0.0002170068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001035633,"about_ca_system_score_gemma":0.00002042535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007106983,"about_ca_topic_score_gemma":0.00002002653,"domain_scores_codex":[0.9993415,0.00001084262,0.0001225128,0.0002571294,0.0001146721,0.000153273],"domain_scores_gemma":[0.999639,0.00002198098,0.00003480972,0.000211616,0.00006310811,0.00002942332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000706206,0.0003170854,0.001469947,0.00005822007,0.00001723709,0.000004068439,0.00007155837,0.0001257901,0.1063919,0.04552396,0.69107,0.1548796],"study_design_scores_gemma":[0.001730069,0.0006432822,0.0201826,0.00002552631,0.00002431678,0.00002725234,0.00001458828,0.03726996,0.3996196,0.03956956,0.5002782,0.0006150095],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01261528,0.00004237837,0.9700466,0.002333967,0.0008788217,0.0003638601,0.000007751508,0.0003390236,0.01337226],"genre_scores_gemma":[0.9525937,0.000002228023,0.02932203,0.0005812052,0.0003038619,0.00007208461,0.00006696038,0.00000785746,0.01705009],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9407246,"threshold_uncertainty_score":0.2789255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01330877352359214,"score_gpt":0.2304237066098354,"score_spread":0.2171149330862432,"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."}}