{"id":"W3008035954","doi":"10.1109/ssci44817.2019.9002739","title":"Distribution Based Feature Mapping for Classifying Count Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Pattern recognition (psychology); Kernel (algebra); Support vector machine; Feature (linguistics); Artificial intelligence; Dirichlet distribution; Maximization; Multinomial distribution; Kernel method; Feature vector; Latent Dirichlet allocation; Machine learning; Data mining; Mathematics; Topic model; Statistics","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":[0.001775204,0.0007101376,0.001013383,0.002652591,0.0004249332,0.001003556,0.001343457,0.0008601505,0.001498176],"category_scores_gemma":[0.009025468,0.0002028699,0.0009048928,0.002617976,0.0007844656,0.002517017,0.0009510947,0.001176572,0.0008655772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005746624,"about_ca_system_score_gemma":0.0004916631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009703402,"about_ca_topic_score_gemma":0.0006312372,"domain_scores_codex":[0.9981909,0.0004996207,0.0001687767,0.0004806828,0.0005117791,0.0001482635],"domain_scores_gemma":[0.9967461,0.001609854,0.0003390948,0.0005615234,0.0006588261,0.00008458627],"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":[0.0003914507,0.000298243,0.01282824,0.0002481625,0.000103378,0.0002748843,0.0002201319,0.08989576,0.02081411,0.01835048,0.00370896,0.8528661],"study_design_scores_gemma":[0.00001310499,0.0001084482,0.005692255,0.00001913557,0.00001709592,0.0003582539,0.00009127893,0.9641604,0.008637448,0.0185284,0.002330082,0.00004411318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02781902,0.0001452356,0.9704572,0.0001051308,0.00003529111,0.00006156095,0.0001911608,0.0007579271,0.0004274843],"genre_scores_gemma":[0.6288537,0.0002463145,0.3676696,0.0001000909,0.00009748703,0.0003386159,0.001323107,0.0001581327,0.001213086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002652591,"threshold_uncertainty_score":0.009388268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05951406172279324,"score_gpt":0.2782676755814202,"score_spread":0.218753613858627,"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."}}