{"id":"W2129718951","doi":"10.1109/wits.1994.513862","title":"Nonparametric classifier design using vector quantization","year":2002,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nonparametric statistics; Vector quantization; Computer science; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Machine learning; Support vector machine; Quantization (signal processing); Data mining; Algorithm; Mathematics; 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":[0.003480224,0.0005643091,0.001598715,0.0009391257,0.0006317409,0.001391623,0.001484865,0.001055993,0.002218153],"category_scores_gemma":[0.009384169,0.0004363777,0.0006344924,0.001030316,0.0007059978,0.001639667,0.001038181,0.001639193,0.001057493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007951451,"about_ca_system_score_gemma":0.001474481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001863349,"about_ca_topic_score_gemma":0.00180124,"domain_scores_codex":[0.997242,0.0009792272,0.0002062104,0.0004133949,0.001013588,0.0001455256],"domain_scores_gemma":[0.996977,0.001069568,0.0002019518,0.0003304506,0.001349936,0.00007125296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002002272,0.00007575635,0.0006478095,0.0001567277,0.00006184174,0.00005048006,0.0001069952,0.1702595,0.01318139,0.04949684,0.006192037,0.7595705],"study_design_scores_gemma":[0.00001786407,0.00009992056,0.0001732843,0.00001069558,0.00000926028,0.00005055072,0.00001287103,0.9769906,0.003040485,0.0164832,0.003093928,0.00001741726],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001024216,0.00006870909,0.9984548,0.00003602944,0.0000262624,0.00002470474,0.00001440818,0.000135254,0.000215587],"genre_scores_gemma":[0.2367561,0.0003737474,0.7589245,0.0002134996,0.0001956459,0.0005295714,0.0003804689,0.0001209016,0.002505486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003480224,"threshold_uncertainty_score":0.01840544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1284965066440079,"score_gpt":0.2882021818433756,"score_spread":0.1597056751993677,"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."}}