{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003465835,0.0001783807,0.0001832198,0.00006969518,0.0002770679,0.0001385716,0.0006702294,0.0001803662,0.00001176066],"category_scores_gemma":[0.00007120297,0.000148013,0.0001238136,0.000321742,0.000175801,0.0002738064,0.0002673081,0.0002473498,0.00009382842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001224157,"about_ca_system_score_gemma":0.00003868243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001900509,"about_ca_topic_score_gemma":3.780185e-7,"domain_scores_codex":[0.9988047,0.00004623147,0.0002134145,0.0004950564,0.0001231048,0.0003174782],"domain_scores_gemma":[0.9992362,0.00005570093,0.0001273037,0.0003501888,0.0001595604,0.000071002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004471179,0.0002801897,0.001322218,0.00006271359,0.00008438283,0.00003444809,0.001215915,0.000004098705,0.007839543,0.4846002,0.04026084,0.4642507],"study_design_scores_gemma":[0.003827109,0.001422359,0.005391148,0.0001301563,0.00006421108,0.0001272051,0.0007182977,0.006309916,0.00408501,0.939916,0.03703596,0.0009725927],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2963046,0.001131775,0.5895637,0.03184403,0.00048688,0.00127666,0.0000254857,0.002920593,0.07644633],"genre_scores_gemma":[0.9637077,0.000505149,0.03228246,0.002751993,0.00004238435,0.00001384453,0.00001178576,0.000008125992,0.0006766028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.667403,"threshold_uncertainty_score":0.6035797,"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."}}