{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001995835,0.0001907953,0.0001933679,0.0001407817,0.001071211,0.0002178506,0.001519447,0.0001016447,0.00005766814],"category_scores_gemma":[0.00003309655,0.0002081007,0.00009547647,0.0006475518,0.0001437336,0.0002796054,0.0002692391,0.0003971663,0.0001761629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004238485,"about_ca_system_score_gemma":0.0001085286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002661608,"about_ca_topic_score_gemma":0.00005005531,"domain_scores_codex":[0.9985252,0.0001306899,0.0003821323,0.0004956509,0.0001918806,0.0002744649],"domain_scores_gemma":[0.995729,0.000740412,0.0001117124,0.003016445,0.0002259673,0.0001764271],"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":[0.000003834899,0.0006704426,0.00009478811,0.00003070182,0.00005511627,0.000003954496,0.001058197,0.0007684207,0.004491794,0.003061203,0.0002416449,0.9895199],"study_design_scores_gemma":[0.001909741,0.0001546081,0.002778692,0.0002542944,0.0001026557,0.0002733213,0.0008020194,0.7858039,0.01374103,0.005094641,0.1879376,0.001147416],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00111745,0.0002012482,0.9894656,0.007312157,0.0001242008,0.000325022,0.00002887788,0.0003482274,0.001077248],"genre_scores_gemma":[0.5690447,0.0007509737,0.4289997,0.0006418737,0.00004652418,0.0002044528,0.00007877562,0.00001736363,0.0002156589],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9883725,"threshold_uncertainty_score":0.84861,"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."}}