{"id":"W2122135337","doi":"10.1016/j.patcog.2007.03.006","title":"On using prototype reduction schemes to optimize dissimilarity-based classification","year":2007,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Discriminant; Computer science; Naive Bayes classifier; Set (abstract data type); Computation; Linear discriminant analysis; Machine learning; Data mining; Support vector machine; Algorithm","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":[0.0007583075,0.0001470583,0.0001069267,0.0002929157,0.000197004,0.0001598773,0.0002718523,0.00009141229,0.00004644776],"category_scores_gemma":[0.0001550141,0.0001487148,0.00004507059,0.0004434456,0.00001834818,0.0003603386,0.00003611838,0.0002087254,0.0002532499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001210393,"about_ca_system_score_gemma":0.00003985886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003615359,"about_ca_topic_score_gemma":0.000004193349,"domain_scores_codex":[0.9985533,0.0001069403,0.0002801843,0.0005070254,0.0003060942,0.0002464322],"domain_scores_gemma":[0.9990093,0.00008093863,0.0001635168,0.0004727011,0.0001497848,0.0001237751],"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":[0.0001213597,0.0002245419,0.001470465,0.00004114898,0.000008201437,0.000004754367,0.000154107,0.0003936702,0.01548742,0.0007568792,0.0001617549,0.9811757],"study_design_scores_gemma":[0.001494267,0.0008970923,0.07634715,0.0004869675,0.00004221597,0.00006439552,0.0001127393,0.8367103,0.07474377,0.004292978,0.003709439,0.001098704],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1514997,0.000003269542,0.845431,0.001216844,0.0002822123,0.0005013267,0.000009044508,0.0002167251,0.000839854],"genre_scores_gemma":[0.8982432,0.000001091782,0.1008426,0.0004490801,0.0001372985,0.00005886609,0.0002334521,0.00001503479,0.00001929598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.980077,"threshold_uncertainty_score":0.6064412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08314667445788243,"score_gpt":0.3323978684400556,"score_spread":0.2492511939821732,"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."}}