{"id":"W1983349993","doi":"10.1016/j.neunet.2006.12.005","title":"The learning vector quantization algorithm applied to automatic text classification tasks","year":2007,"lang":"en","type":"article","venue":"Neural Networks","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; McGill University","keywords":"Learning vector quantization; Computer science; Artificial intelligence; Classifier (UML); Artificial neural network; Word-sense disambiguation; Categorization; Linde–Buzo–Gray algorithm; Text categorization; Machine learning; Word (group theory); Natural language processing; Pattern recognition (psychology); Vector quantization; Mathematics; WordNet","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002721036,0.0005407652,0.001211904,0.001318652,0.0007354119,0.001184923,0.001267029,0.001076578,0.003176144],"category_scores_gemma":[0.007320526,0.0003133346,0.0005132307,0.002607695,0.0005690123,0.001807026,0.000810207,0.001651829,0.001467781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000841377,"about_ca_system_score_gemma":0.001793717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007613204,"about_ca_topic_score_gemma":0.005919899,"domain_scores_codex":[0.9980572,0.000636417,0.0002461133,0.0002992599,0.0006674416,0.00009345454],"domain_scores_gemma":[0.9974632,0.0009684917,0.00009944834,0.0003438172,0.001068327,0.00005667725],"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":[0.000143103,0.00008235258,0.0005915553,0.0001671414,0.00004837215,0.00002872237,0.00007223318,0.0184757,0.007407563,0.0117642,0.008830606,0.9523886],"study_design_scores_gemma":[0.00008841324,0.0002148443,0.001639084,0.00008788572,0.00005864924,0.0001887763,0.00007013982,0.9209679,0.01896534,0.04273682,0.01493035,0.00005173739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01149018,0.002299721,0.9807321,0.0005180922,0.0006244482,0.000197028,0.000351956,0.00184414,0.001942343],"genre_scores_gemma":[0.1694007,0.001636605,0.8197133,0.0003808842,0.000343949,0.0004247065,0.001219782,0.0002052846,0.006674839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007613204,"threshold_uncertainty_score":0.01513779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02001477650850889,"score_gpt":0.2704345014242594,"score_spread":0.2504197249157505,"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."}}