{"id":"W2140389641","doi":"10.1109/tnn.2004.841784","title":"Optimizing the Kernel in the Empirical Feature Space","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":318,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Kernel (algebra); Measure (data warehouse); Kernel embedding of distributions; Feature (linguistics); Kernel method; Computer science; Artificial intelligence; Feature vector; Pattern recognition (psychology); Euclidean space; Variable kernel density estimation; Graph kernel; Mathematics; Tree kernel; Algorithm; Data mining; Support vector machine","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":[0.002208101,0.0005967037,0.001073169,0.0005352602,0.0003160085,0.000961961,0.001014537,0.0007949364,0.0005942536],"category_scores_gemma":[0.00806758,0.0003621916,0.0005694441,0.0007285956,0.001124608,0.002535003,0.001247752,0.00108682,0.0004178924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000693208,"about_ca_system_score_gemma":0.0008143298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009213217,"about_ca_topic_score_gemma":0.000539956,"domain_scores_codex":[0.9984621,0.0005115204,0.0001062636,0.000348829,0.0004733462,0.00009789715],"domain_scores_gemma":[0.997942,0.000967649,0.0001942329,0.0004195066,0.0004272832,0.00004922722],"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":[0.0002290545,0.0001290218,0.001477671,0.0001840233,0.000118532,0.00008569728,0.0001464024,0.6702343,0.02863327,0.07052343,0.001182941,0.2270556],"study_design_scores_gemma":[0.000005979782,0.00002545554,0.0002430865,0.000002927773,0.000006276829,0.00002875706,0.000007564885,0.9843715,0.004596109,0.01014476,0.0005589854,0.000008624585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007551658,0.00005024902,0.9921036,0.00003451089,0.000004344961,0.000006237382,0.000007704769,0.00009251134,0.0001491922],"genre_scores_gemma":[0.3780524,0.0002316727,0.6196038,0.00005811968,0.0000425704,0.000107091,0.0001984063,0.0002221872,0.001483812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002208101,"threshold_uncertainty_score":0.01167768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0439986019124259,"score_gpt":0.2785864826335006,"score_spread":0.2345878807210746,"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."}}