{"id":"W3175244446","doi":"10.1145/3409264","title":"Pinball Loss Twin Support Vector Clustering","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; IXICO; National Institutes of Health; H. Lundbeck A/S; Pfizer; Novartis Pharmaceuticals Corporation; Servier; Indian Institute of Technology Indore; National Institute on Aging; Alzheimer's Association; Merck; GE Healthcare; BioClinica; Eli Lilly and Company","keywords":"Cluster analysis; Fuzzy clustering; Computer science; Correlation clustering; Data stream clustering; CURE data clustering algorithm; Hinge loss; Canopy clustering algorithm; Noise (video); Benchmark (surveying); Artificial intelligence; Pattern recognition (psychology); Data mining; Stability (learning theory); Clustering high-dimensional data; Support vector machine; Machine learning","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":[0.002425035,0.001300586,0.001933625,0.00189638,0.000927921,0.002147479,0.003548076,0.001864993,0.001936342],"category_scores_gemma":[0.008429892,0.0004919457,0.001094915,0.002452083,0.001214884,0.002792699,0.00231852,0.002009159,0.0009077875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376439,"about_ca_system_score_gemma":0.001957361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006394583,"about_ca_topic_score_gemma":0.003794886,"domain_scores_codex":[0.9973272,0.0005580651,0.0001821286,0.0006116448,0.001070458,0.0002504049],"domain_scores_gemma":[0.9971655,0.0006493133,0.0002582307,0.0004679496,0.001308613,0.0001504195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000392116,0.0001121751,0.002337936,0.0001677034,0.000145251,0.0001456383,0.0001609609,0.6184476,0.006039714,0.01617641,0.005707308,0.3501672],"study_design_scores_gemma":[0.000004496912,0.00002911068,0.0001352506,0.000005576668,0.000004994452,0.00002583642,0.00001214301,0.995882,0.001122299,0.002290207,0.0004798793,0.000008228916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01354492,0.0003562736,0.984382,0.0001290778,0.0000528378,0.00007843148,0.0001111281,0.0006074687,0.0007378359],"genre_scores_gemma":[0.5446091,0.0005798806,0.4473419,0.0002997124,0.0001072409,0.0003258592,0.001591924,0.000292587,0.004851849],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006394583,"threshold_uncertainty_score":0.01282495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02953463740979331,"score_gpt":0.292046506580547,"score_spread":0.2625118691707538,"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."}}