{"id":"W2005890246","doi":"10.1109/ijcnn.2006.246739","title":"A Heuristic for Free Parameter Optimization with Support Vector Machines","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Maxima and minima; Computer science; Hyperparameter optimization; Support vector machine; Heuristic; Simulated annealing; Generalization; Mathematical optimization; Machine learning; Gradient descent; Selection (genetic algorithm); Incremental heuristic search; Artificial intelligence; Algorithm; Search algorithm; Mathematics; Beam search; Artificial neural network","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":[0.0001867999,0.0002324955,0.0002951758,0.00009848049,0.0001328123,0.0001773909,0.0002634605,0.00004836448,0.0001262731],"category_scores_gemma":[0.00008317363,0.0001400082,0.0001417962,0.0001915169,0.0001184294,0.000126311,0.00002842242,0.0002212152,0.00001310965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005001123,"about_ca_system_score_gemma":0.00003892304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007955042,"about_ca_topic_score_gemma":0.000008555594,"domain_scores_codex":[0.998584,0.000007482103,0.0003393067,0.0003350771,0.0004336856,0.0003004813],"domain_scores_gemma":[0.9988627,0.00007470581,0.0002293702,0.0001522579,0.0006147874,0.00006613637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009282026,0.001141322,0.07239671,0.0003795001,0.001238384,0.0001292606,0.000405213,0.4056984,0.006686652,0.07266457,0.4211286,0.008849336],"study_design_scores_gemma":[0.001518681,0.0007499793,0.005443939,0.0002842905,0.0002982604,0.0002191619,0.000033515,0.9854423,0.0007093782,0.003777151,0.001236179,0.000287202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8582329,0.00006571732,0.03351143,0.06657301,0.001156155,0.00181159,0.00009506576,0.0004432706,0.03811088],"genre_scores_gemma":[0.986865,0.00001493753,0.005719333,0.001321586,0.001405973,0.00009650893,0.00009716759,0.00003390804,0.004445585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5797439,"threshold_uncertainty_score":0.570937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03147208949691202,"score_gpt":0.2770154457785389,"score_spread":0.2455433562816269,"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."}}