{"id":"W4307993127","doi":"10.32920/21428721","title":"Knowledge-Based Green’s Kernel for Support Vector Regression","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Kernel (algebra); Radial basis function kernel; Polynomial kernel; Kernel method; Regularization (linguistics); Kernel embedding of distributions; Regularization perspectives on support vector machines; Mathematics; Artificial intelligence; Computer science; Benchmark (surveying); Pattern recognition (psychology); Machine learning; Inverse problem; Pure mathematics; Mathematical analysis; Tikhonov regularization; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001736989,0.0008581402,0.001359686,0.001162726,0.0003543485,0.001309359,0.001485207,0.001726462,0.002074699],"category_scores_gemma":[0.00772677,0.0004005009,0.001161478,0.001300722,0.001210419,0.002584899,0.001211778,0.001941574,0.001281051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016742,"about_ca_system_score_gemma":0.0009358454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001908887,"about_ca_topic_score_gemma":0.001047178,"domain_scores_codex":[0.9984992,0.0004353248,0.00008757469,0.0002885477,0.0005834675,0.0001058783],"domain_scores_gemma":[0.9973846,0.001279961,0.0002096532,0.000416112,0.0006343611,0.00007534772],"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":[0.0001948169,0.000108365,0.0007615825,0.0002375499,0.0001222492,0.0001900794,0.0001172133,0.5752333,0.01406004,0.09193603,0.003582488,0.3134563],"study_design_scores_gemma":[0.000003549518,0.00002332908,0.0001120098,0.000009747971,0.00000638786,0.0000365479,0.000005189496,0.9855409,0.002576884,0.01055187,0.001123014,0.00001059646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002538061,0.0001421508,0.9966208,0.00006055045,0.0000159755,0.000009273773,0.00002033772,0.0001719887,0.0004208962],"genre_scores_gemma":[0.3848558,0.001062359,0.6061679,0.0002851918,0.0001600742,0.0001541494,0.0006090492,0.0003140591,0.0063914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002074699,"threshold_uncertainty_score":0.009186149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04814208555622573,"score_gpt":0.3149314763788532,"score_spread":0.2667893908226274,"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."}}