{"id":"W2138776269","doi":"10.1109/ijcnn.2002.1007489","title":"Clustering unlabeled data with SOMs improves classification of labeled real-world data","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Labeled data; Cluster analysis; Artificial intelligence; Benchmark (surveying); Pattern recognition (psychology); Artificial neural network; Self-organizing map; Multilayer perceptron; Semi-supervised learning; Supervised learning; Cluster (spacecraft); Perceptron; Unsupervised learning; Data mining; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002876539,0.0001038087,0.000143923,0.00005019059,0.00008548416,0.00008905668,0.00249311,0.00001859869,0.000008126153],"category_scores_gemma":[0.00001610454,0.00007668864,0.000008754,0.0007371104,0.00006042513,0.0007692106,0.0007600644,0.00007969062,0.000008549902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001060895,"about_ca_system_score_gemma":0.00006376112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001629481,"about_ca_topic_score_gemma":0.002137471,"domain_scores_codex":[0.9987616,0.00004129882,0.0002419405,0.0005949853,0.0001723202,0.0001878435],"domain_scores_gemma":[0.9957321,0.00008785276,0.0001637424,0.003897808,0.00005794681,0.00006050056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002278052,0.0003607901,0.00400832,0.00005320064,0.00008449933,0.00000333432,0.00007372208,0.000293024,0.0810982,0.7720122,0.01474226,0.1272476],"study_design_scores_gemma":[0.0004570209,0.00006464736,0.008181254,0.00002240556,0.000020008,0.000005565541,0.00003127157,0.9719679,0.003544481,0.0006861169,0.01477969,0.0002396947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02708457,0.00003871006,0.9601941,0.001717442,0.0000927732,0.0003997242,0.00003237409,0.0002059392,0.01023434],"genre_scores_gemma":[0.8319251,0.00005096016,0.1667647,0.0001137334,0.00003017071,0.00001188339,0.0001216697,0.000009852569,0.0009719542],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9716748,"threshold_uncertainty_score":0.463286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09779251755219591,"score_gpt":0.3197640324068891,"score_spread":0.2219715148546932,"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."}}