{"id":"W76477875","doi":"","title":"Sorted Kernel Matrices as Cluster Validity Indexes.","year":2009,"lang":"en","type":"article","venue":"European Society for Fuzzy Logic and Technology Conference","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Kernel (algebra); Cluster analysis; String kernel; Sorting; Computer science; Kernel embedding of distributions; Variable kernel density estimation; Kernel method; Metric (unit); Mathematics; Pattern recognition (psychology); Polynomial kernel; Fuzzy clustering; Kernel principal component analysis; Radial basis function kernel; Similarity (geometry); Artificial intelligence; Data mining; Algorithm; Support vector machine; Combinatorics; Image (mathematics)","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":[0.005996017,0.0009028793,0.0009951926,0.004720669,0.0009821657,0.003995376,0.001588062,0.001134009,0.003450299],"category_scores_gemma":[0.03588847,0.0004102711,0.0006976142,0.004464892,0.001857704,0.004973918,0.001991379,0.001398725,0.001334964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001880894,"about_ca_system_score_gemma":0.001517608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001284221,"about_ca_topic_score_gemma":0.001437334,"domain_scores_codex":[0.9945554,0.002405426,0.0004449502,0.0008155285,0.001507714,0.0002710436],"domain_scores_gemma":[0.9859315,0.007606135,0.001479757,0.002024844,0.002694745,0.0002629897],"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":[0.0008452612,0.0002451249,0.008346186,0.0009298684,0.0003909706,0.000174505,0.00110778,0.1208019,0.01541595,0.3909987,0.007869535,0.4528742],"study_design_scores_gemma":[0.00004616784,0.0002439539,0.007045972,0.0001755487,0.0001090276,0.0003607305,0.0007357951,0.6202233,0.01791339,0.3398572,0.01317633,0.0001125553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03069568,0.0008672935,0.9631102,0.0003211638,0.0000907494,0.000252713,0.0005216782,0.0005796538,0.003560847],"genre_scores_gemma":[0.4610225,0.0005131625,0.534247,0.0001445402,0.0001268586,0.0004776629,0.001079486,0.0001893514,0.002199512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005996017,"threshold_uncertainty_score":0.03171039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03277795266621557,"score_gpt":0.2588354751814697,"score_spread":0.2260575225152541,"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."}}