{"id":"W2143871051","doi":"10.1109/mwscas.1993.343336","title":"On pattern classification using linear-output neural network classifiers","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Subspace topology; Linear discriminant analysis; Artificial neural network; Discriminant; Value (mathematics); Artificial intelligence; Set (abstract data type); Mean squared error; Computer science; Mathematics; Pattern recognition (psychology); Algorithm; Machine learning; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002018158,0.001007927,0.001010834,0.001604421,0.0003493472,0.002107343,0.00111331,0.001517855,0.002619903],"category_scores_gemma":[0.009097959,0.0002903249,0.0004981494,0.002331207,0.001644139,0.002920345,0.001133819,0.00116079,0.001152679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000601877,"about_ca_system_score_gemma":0.0004595934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001854704,"about_ca_topic_score_gemma":0.001112254,"domain_scores_codex":[0.9986222,0.0004843487,0.0001044619,0.0002540041,0.0004595563,0.00007550729],"domain_scores_gemma":[0.9971517,0.002081921,0.0001368668,0.0001948158,0.0004079133,0.00002684787],"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":[0.0001787426,0.00008231789,0.001083785,0.0004241891,0.0001829349,0.0001915767,0.000158659,0.2697582,0.004677338,0.07944153,0.006640209,0.6371806],"study_design_scores_gemma":[0.00001303256,0.00004826826,0.0003876494,0.00007715172,0.00002562691,0.00007127336,0.00002085491,0.9209986,0.002366254,0.07062892,0.005341087,0.00002121171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004017625,0.002849506,0.9893289,0.0003752017,0.0001315652,0.00003317871,0.00004909354,0.000257432,0.00295744],"genre_scores_gemma":[0.3018406,0.0132431,0.6681796,0.001067597,0.001715351,0.0004414289,0.0008354128,0.0002091897,0.01246767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002619903,"threshold_uncertainty_score":0.01067317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09408212244263889,"score_gpt":0.2759412185551183,"score_spread":0.1818590961124794,"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."}}