{"id":"W2111697789","doi":"10.1109/iwfhr.2002.1030925","title":"Empirical error based optimization of SVM kernels: application to digit image recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Concordia University","funders":"","keywords":"Support vector machine; Kernel (algebra); Polynomial; Computer science; Convergence (economics); NIST; Gradient descent; Algorithm; Kernel method; Mathematical optimization; Artificial intelligence; Pattern recognition (psychology); Mathematics; Artificial neural network; Speech recognition","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":[0.0001069385,0.00008041583,0.00009060716,0.00006093817,0.00005793383,0.00005783936,0.0002529755,0.00003757777,0.00005736705],"category_scores_gemma":[0.00003300529,0.00007316625,0.00003938529,0.0006371156,0.00001763788,0.0002519985,0.00003262154,0.000044575,0.00008509101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001883297,"about_ca_system_score_gemma":0.00003098622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006403504,"about_ca_topic_score_gemma":0.000002755066,"domain_scores_codex":[0.9991838,0.00003681027,0.0002135987,0.0002923258,0.0001414032,0.0001320021],"domain_scores_gemma":[0.9992625,0.00006448229,0.00007290971,0.0003776232,0.0001360902,0.00008641858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004305288,0.001341276,0.001841127,0.00006351601,0.00002548682,0.000002594389,0.0002893457,0.6288607,0.02518804,0.04820687,0.0342348,0.2599033],"study_design_scores_gemma":[0.0002891871,0.00008083207,0.0006346842,0.00001282628,0.000007104533,0.000003977097,0.00001193685,0.9308544,0.05458703,0.004296222,0.008992808,0.0002289638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003761658,0.000003370086,0.9877126,0.001816283,0.00003048719,0.0003352817,0.000004038578,0.0001139411,0.006222311],"genre_scores_gemma":[0.5366614,0.000002007105,0.4619842,0.001075312,0.00001925722,0.0001124346,0.00002163321,0.000007070906,0.0001166253],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5328997,"threshold_uncertainty_score":0.2983633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03821892080945861,"score_gpt":0.2980986056609436,"score_spread":0.259879684851485,"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."}}