{"id":"W1832226433","doi":"10.1007/3-540-45486-1_29","title":"Typical Example Selection for Learning Classifiers","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mahalanobis distance; Computer science; Artificial intelligence; Set (abstract data type); Selection (genetic algorithm); Machine learning; Gaussian; Training set; Measure (data warehouse); Pattern recognition (psychology); Labeled data; Data mining","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":[0.0009081092,0.0003845143,0.0003669061,0.0006390386,0.000483726,0.0005635683,0.001808731,0.0003483621,0.0000507083],"category_scores_gemma":[0.0001734452,0.0003692489,0.0001200727,0.0005010386,0.0002967464,0.0005048576,0.000312777,0.00108979,0.00007202094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000257075,"about_ca_system_score_gemma":0.0004124348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000481208,"about_ca_topic_score_gemma":0.00007206888,"domain_scores_codex":[0.9968361,0.00005742835,0.0004144055,0.001528838,0.0005895476,0.0005736952],"domain_scores_gemma":[0.9980836,0.0005952316,0.0002402018,0.0007697176,0.0001603429,0.0001509232],"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":[0.00001817898,0.00001563722,0.000199577,0.00002037381,0.000006136945,0.000002510184,0.0002234607,0.01997605,0.00009236763,0.043893,0.00006021309,0.9354925],"study_design_scores_gemma":[0.0002334664,0.0002363946,0.0004899762,0.00009433827,0.000007211197,0.00003242361,1.092872e-7,0.8389398,0.0001522569,0.08056507,0.07879814,0.0004508003],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00006507555,0.0001081025,0.9930369,0.0007632407,0.0007094984,0.0003514622,0.000002865601,0.0003118388,0.004651029],"genre_scores_gemma":[0.1705239,0.0001023432,0.8236011,0.001174475,0.001206632,0.00005053802,0.00009509787,0.00007866877,0.003167269],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9350417,"threshold_uncertainty_score":0.999876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02776638664119958,"score_gpt":0.2658417962126388,"score_spread":0.2380754095714392,"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."}}