{"id":"W2052985314","doi":"10.1016/s0893-6080(00)00049-6","title":"Iterative fast orthogonal search algorithm for MDL-based training of generalized single-layer networks","year":2000,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Minimum description length; Algorithm; Basis (linear algebra); Basis function; Mathematics; Pruning; Selection (genetic algorithm); Computer science; Iterative method; Mathematical optimization; Artificial intelligence","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":[0.0002884512,0.0003361821,0.0004486493,0.00008204258,0.0003427228,0.0002126038,0.000882095,0.0001792203,0.0001170092],"category_scores_gemma":[0.000004987832,0.000303498,0.0002853599,0.0008276773,0.000138411,0.0003388822,0.00009186084,0.0004027282,0.000003655177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003175647,"about_ca_system_score_gemma":0.00005040612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001060673,"about_ca_topic_score_gemma":0.00000991854,"domain_scores_codex":[0.9973918,0.0001636892,0.0005730211,0.0007097806,0.0003354184,0.0008263343],"domain_scores_gemma":[0.9984018,0.0004420802,0.000157624,0.0005824062,0.0001888612,0.0002272451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002266362,0.00005914607,0.00002096532,0.000002826357,0.00001407351,0.000003866624,0.00007133418,0.4838223,0.0001343083,0.0009272141,0.0006171567,0.5143042],"study_design_scores_gemma":[0.0009731319,0.0002686413,0.0001519944,0.00003356344,0.00001585066,0.00001443906,0.000008547764,0.994682,0.0003780084,0.0002375655,0.002923609,0.0003127046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0283839,0.0003077966,0.9689652,0.0007433392,0.0003342299,0.0006941627,0.00002238777,0.000176347,0.0003725912],"genre_scores_gemma":[0.9055679,0.00003207954,0.0910914,0.00161092,0.001020145,0.0001769976,0.0000911711,0.00004256086,0.0003667622],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8778738,"threshold_uncertainty_score":0.9999417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04963424689951592,"score_gpt":0.280924202035542,"score_spread":0.2312899551360261,"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."}}