{"id":"W2137889427","doi":"10.1109/ijcnn.2000.861455","title":"A Neural Support Vector Network architecture with adaptive kernels","year":2000,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Support vector machine; Kernel (algebra); Artificial intelligence; Artificial neural network; Similarity (geometry); Computer science; Similarity measure; Pattern recognition (psychology); Kernel method; Measure (data warehouse); Machine learning; Least squares support vector machine; Relevance vector machine; Simple (philosophy); Discriminant; Mathematics; Data mining","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":[0.000530638,0.0004615262,0.0005545706,0.0003043758,0.0002884527,0.0008050539,0.001257182,0.001174399,0.002186111],"category_scores_gemma":[0.001418124,0.0002614115,0.0003398711,0.0005155725,0.0002774735,0.001147012,0.0007331382,0.0007969391,0.001170756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004175776,"about_ca_system_score_gemma":0.0005409131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002515802,"about_ca_topic_score_gemma":0.002421418,"domain_scores_codex":[0.9997752,0.00004053202,0.00001526138,0.00005958258,0.00007854414,0.00003084813],"domain_scores_gemma":[0.9996307,0.00007380239,0.00002637535,0.00003870087,0.0002012692,0.00002912533],"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":[0.0003625015,0.0001954277,0.001051695,0.0001559821,0.0001014884,0.0002189218,0.00008481681,0.5220562,0.02386041,0.0151672,0.004622365,0.4321229],"study_design_scores_gemma":[0.000009945479,0.00006010895,0.0001177124,0.000007702741,0.00001159812,0.00002973141,0.000003497139,0.9950068,0.001799657,0.002029063,0.0009172849,0.000006845957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05857348,0.001066211,0.9316654,0.0005209441,0.0002133728,0.00007625132,0.0001027748,0.001522639,0.006258918],"genre_scores_gemma":[0.7177172,0.0007002502,0.2700589,0.0002382288,0.0001190313,0.0001402881,0.0002808403,0.00005128949,0.01069394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002515802,"threshold_uncertainty_score":0.007313311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009667968102555895,"score_gpt":0.2086140740743474,"score_spread":0.1989461059717915,"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."}}