{"id":"W2152733965","doi":"10.1109/mlsp.2008.4685450","title":"A data-driven mixture kernel for count data classification using support vector machines","year":2008,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Computer science; Kernel (algebra); Machine learning; Kernel method; Artificial intelligence; Context (archaeology); Structured support vector machine; Count data; Relevance vector machine; Pattern recognition (psychology); Data modeling; Least squares support vector machine; Dirichlet distribution; Data mining; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005725221,0.0001651826,0.0002099231,0.00005882522,0.0002153656,0.0001100912,0.003171001,0.00009337967,0.00002555368],"category_scores_gemma":[0.00008004499,0.0001303499,0.00003650273,0.0001955813,0.00004868856,0.001248153,0.000931165,0.0001090782,0.00001160103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003005696,"about_ca_system_score_gemma":0.0002460066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005735719,"about_ca_topic_score_gemma":0.00003799219,"domain_scores_codex":[0.9982918,0.0000697282,0.0002674672,0.000859138,0.0002400278,0.0002718441],"domain_scores_gemma":[0.9963662,0.0001077493,0.0001178732,0.003201895,0.0001045382,0.0001017271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006571072,0.0004965573,0.001662024,0.0001731334,0.000191384,0.00007730874,0.001343928,0.00008597064,0.02516999,0.3428838,0.4281969,0.1996533],"study_design_scores_gemma":[0.0002421969,0.00002658145,0.0008798043,0.000006594591,0.00001851889,0.0001020752,0.000003010028,0.9527619,0.0001286784,0.002257808,0.04338396,0.0001888977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006321325,0.00007631105,0.9961643,0.001100802,0.0004023422,0.0003258085,0.0004732427,0.0001393767,0.0006856989],"genre_scores_gemma":[0.02908723,0.00002511379,0.9684286,0.0006926764,0.000260796,0.000009146458,0.0007971133,0.0000167151,0.0006826099],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9526759,"threshold_uncertainty_score":0.5892562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2093161460271405,"score_gpt":0.3688335232462169,"score_spread":0.1595173772190764,"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."}}