{"id":"W3039953684","doi":"10.48550/arxiv.2007.02572","title":"A Novel Random Forest Dissimilarity Measure for Multi-View Learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"European Regional Development Fund; Région Normandie; European Commission","keywords":"Random forest; Large margin nearest neighbor; Margin (machine learning); Metric (unit); Machine learning; Computer science; Dimension (graph theory); Artificial intelligence; Exploit; Context (archaeology); Measure (data warehouse); Task (project management); k-nearest neighbors algorithm; Sample (material); Supervised learning; Data mining; Pattern recognition (psychology); Mathematics; Artificial neural network; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002976757,0.0003151481,0.0004336448,0.0001204328,0.0002915443,0.0001658987,0.001236523,0.0003181138,0.00001355428],"category_scores_gemma":[0.0002251202,0.0003339098,0.0003809175,0.0003178085,0.00005253532,0.0003653285,0.001283488,0.0007453716,0.00004829621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007838243,"about_ca_system_score_gemma":0.0001666532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005523864,"about_ca_topic_score_gemma":0.0001040722,"domain_scores_codex":[0.9981256,0.0001204113,0.0002064414,0.001111366,0.0001064909,0.0003297553],"domain_scores_gemma":[0.998583,0.0001555973,0.000251874,0.0005567993,0.0002270182,0.0002257688],"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":[0.001826834,0.001745468,0.02083704,0.003439793,0.001109739,0.00057168,0.003092045,0.8443593,0.004948879,0.09200715,0.007132928,0.0189292],"study_design_scores_gemma":[0.00323025,0.00005632749,0.0009904887,0.0003448566,0.00009443941,0.000002580121,0.00005712236,0.9851431,0.000208903,0.006912521,0.002485132,0.0004742538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01213945,0.00008396678,0.9856039,0.0004031964,0.0004053139,0.0007001514,0.00003695862,0.0003336445,0.000293468],"genre_scores_gemma":[0.9737131,0.0001261486,0.025189,0.0001930092,0.00008482257,0.000008604387,0.00007531153,0.00002322506,0.0005867876],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9615737,"threshold_uncertainty_score":0.9999113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.19719137147111,"score_gpt":0.226092966290718,"score_spread":0.02890159481960797,"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."}}