{"id":"W3171070551","doi":"","title":"A Precise Performance Analysis of Support Vector Regression","year":2021,"lang":"en","type":"article","venue":"King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology)","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Support vector machine; Regression analysis; Mathematics; Regression; Linear regression; Star (game theory); Statistics; Computer science; Algorithm; Artificial intelligence; Mathematical analysis","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":[0.02409244,0.002752124,0.002650061,0.001950568,0.0007818696,0.002954863,0.002178287,0.00343507,0.002884399],"category_scores_gemma":[0.1808383,0.0007532042,0.0009700587,0.001839051,0.003096801,0.006602407,0.003825398,0.004676559,0.001333773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001460167,"about_ca_system_score_gemma":0.001371502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001045669,"about_ca_topic_score_gemma":0.0003903318,"domain_scores_codex":[0.9741167,0.01441648,0.001028968,0.002464073,0.006937642,0.001036032],"domain_scores_gemma":[0.8621758,0.1138294,0.005802423,0.008327201,0.008883216,0.0009820261],"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":[0.0007524808,0.0001541399,0.005613959,0.0006335152,0.000239979,0.0002941226,0.0002380944,0.742903,0.009607448,0.08609287,0.002119707,0.1513507],"study_design_scores_gemma":[0.00001475686,0.0002631943,0.0009697927,0.00008453332,0.00002423535,0.0001672241,0.00003867487,0.9720193,0.004021982,0.0215257,0.0008408286,0.00002979365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02465668,0.003865304,0.9656689,0.001330561,0.0001413455,0.00008687666,0.000103868,0.0005348659,0.003611666],"genre_scores_gemma":[0.7967106,0.003434686,0.1941336,0.0006192518,0.0008071661,0.0002971669,0.0005633176,0.0005273966,0.002906776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02409244,"threshold_uncertainty_score":0.1274145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004851419409060227,"score_gpt":0.1900184164693654,"score_spread":0.1851669970603052,"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."}}